Analyses

Author

Nicholas Vietto

pacman::p_load(tidyverse, janitor, ppcor, foreign, renv, ltm, pastecs)

data <- read.spss("AutonomicFinal042523.sav", use.value.labels=TRUE, max.value.labels=Inf, to.data.frame=TRUE)

Recodes and Scales

Dummy Codes

These numbers contain the missing values, to get sample descriptive (e.g., race/ethnicity) the numbers must be calculated with the data frames in the Wrangling section. in other words, run all chunks then run tabyl(FSFSurveyT1$race_eth) in the console.

# Sex

data$Genderfactor <-as.factor(data$Gender)
data$GenderNumb <-as.numeric(data$Genderfactor)

table(data$GenderNumb)

 1  2 
80 26 
data$Female <- data$GenderNumb
data$Female = ifelse(data$GenderNumb == 1, 1, data$Female)
data$Female = ifelse(data$GenderNumb == 2, 0, data$Female)
table(data$Female)

 0  1 
26 80 
table(data$GenderNumb)

 1  2 
80 26 
data$Male <- data$GenderNumb
data$Male = ifelse(data$GenderNumb == 1, 0, data$Male)
data$Male = ifelse(data$GenderNumb == 2, 1, data$Male)
table(data$Male)

 0  1 
80 26 
# Race

# Dem percents 
table(data$race_eth)

American Indian/Native American Asian or Pacific Islander       
                              1                               6 
Black/African American          Hispanic                        
                             10                              26 
Multiracial                     Other                           
                              4                               1 
White                           
                             58 
data$race_eth1 <- as.factor(data$race_eth)   
data$race_eth2 <- as.numeric(data$race_eth1)
table(data$race_eth2)

 1  2  3  4  5  6  7 
 1  6 10 26  4  1 58 
data$race_ethDC <- data$race_eth2
data$race_ethDC = ifelse(data$race_eth2 == 7, 8, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 6, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 5, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 4, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 3, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 2, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 1, 0, data$race_ethDC)
table(data$race_ethDC)

 0  8 
48 58 
data$White = data$race_ethDC
data$White <- as.factor(data$White)
data$White = ifelse(data$race_ethDC == 8, 1, data$White)
data$White = ifelse(data$race_ethDC == 0, 0, data$White)
table(data$White)

 0  1 
48 58 

SRP Reverse Codes

IPM (16, 24, 31, 38, 61) CA (11, 19, 23, 26, 44) ELS (14, 22, 25, 36, 47) ASB (5, 6, 18, 21, 34, 46)

Citation: Paulhus, D.L., Neumann, C. S., & Hare, R.D. (in press). Manual for the Self-Report Psychopathy scale 4th edition. Toronto: Multi-Health Systems.

Code Reference

# IPM

table(data$SRP_16n)

 1  2  3  4  5 
 2 18 36 33 17 
data$SRP16nRev = data$SRP_16n
data$SRP16nRev = ifelse(data$SRP_16n == 1, 5, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 2, 4, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 4, 2, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 5, 1, data$SRP16nRev)
table(data$SRP16nRev)

 1  2  3  4  5 
17 33 36 18  2 
table(data$SRP_24n)

 1  2  3  4  5 
 5 19 24 46 12 
data$SRP24nRev = data$SRP_24n
data$SRP24nRev = ifelse(data$SRP_24n == 1, 5, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 2, 4, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 4, 2, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 5, 1, data$SRP24nRev)
table(data$SRP24nRev)

 1  2  3  4  5 
12 46 24 19  5 
table(data$SRP_31n)

 1  2  3  4  5 
 6 15 46 30  9 
data$SRP31nRev = data$SRP_31n
data$SRP31nRev = ifelse(data$SRP_31n == 1, 5, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 2, 4, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 4, 2, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 5, 1, data$SRP31nRev)
table(data$SRP31nRev)

 1  2  3  4  5 
 9 30 46 15  6 
table(data$SRP_38n)

 1  2  3  4  5 
 4 18 28 36 20 
data$SRP38nRev = data$SRP_38n
data$SRP38nRev = ifelse(data$SRP_38n == 1, 5, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 2, 4, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 4, 2, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 5, 1, data$SRP38nRev)
table(data$SRP38nRev)

 1  2  3  4  5 
20 36 28 18  4 
table(data$SRP_61n)

 1  2  3  4  5 
 5 14 15 37 35 
data$SRP61nRev = data$SRP_61n
data$SRP61nRev = ifelse(data$SRP_61n == 1, 5, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 2, 4, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 4, 2, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 5, 1, data$SRP61nRev)
table(data$SRP61nRev)

 1  2  3  4  5 
35 37 15 14  5 
# CA

table(data$SRP_11n)

 1  2  3  4  5 
 2  7 10 43 44 
data$SRP11nRev = data$SRP_11n
data$SRP11nRev = ifelse(data$SRP_11n == 1, 5, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 2, 4, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 4, 2, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 5, 1, data$SRP11nRev)
table(data$SRP11nRev)

 1  2  3  4  5 
44 43 10  7  2 
table(data$SRP_19n)

 2  3  4  5 
 8 14 54 29 
data$SRP19nRev = data$SRP_19n
data$SRP19nRev = ifelse(data$SRP_19n == 1, 5, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 2, 4, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 4, 2, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 5, 1, data$SRP19nRev)
table(data$SRP19nRev)

 1  2  3  4 
29 54 14  8 
table(data$SRP_23n)

 1  2  3  4  5 
28 37 15 14 12 
data$SRP23nRev = data$SRP_23n
data$SRP23nRev = ifelse(data$SRP_23n == 1, 5, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 2, 4, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 4, 2, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 5, 1, data$SRP23nRev)
table(data$SRP23nRev)

 1  2  3  4  5 
12 14 15 37 28 
table(data$SRP_26n)

 2  3  4  5 
 6 25 49 26 
data$SRP26nRev = data$SRP_26n
data$SRP26nRev = ifelse(data$SRP_26n == 1, 5, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 2, 4, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 4, 2, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 5, 1, data$SRP26nRev)
table(data$SRP26nRev)

 1  2  3  4 
26 49 25  6 
table(data$SRP_44n)

 1  2  3  4  5 
 1  6 20 53 26 
data$SRP44nRev = data$SRP_44n
data$SRP44nRev = ifelse(data$SRP_44n == 1, 5, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 2, 4, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 4, 2, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 5, 1, data$SRP44nRev)
table(data$SRP44nRev)

 1  2  3  4  5 
26 53 20  6  1 
# ELS

table(data$SRP_14n)

 1  2  3  4  5 
 8 13 17 40 27 
data$SRP14nRev = data$SRP_14n
data$SRP14nRev = ifelse(data$SRP_14n == 1, 5, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 2, 4, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 4, 2, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 5, 1, data$SRP14nRev)
table(data$SRP14nRev)

 1  2  3  4  5 
27 40 17 13  8 
table(data$SRP_22n)

 1  2  3  4  5 
 5 29 15 35 22 
data$SRP22nRev = data$SRP_22n
data$SRP22nRev = ifelse(data$SRP_22n == 1, 5, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 2, 4, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 4, 2, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 5, 1, data$SRP22nRev)
table(data$SRP22nRev)

 1  2  3  4  5 
22 35 15 29  5 
table(data$SRP_25n)

 1  2  3  4  5 
 9 35 37 16  9 
data$SRP25nRev = data$SRP_25n
data$SRP25nRev = ifelse(data$SRP_25n == 1, 5, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 2, 4, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 4, 2, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 5, 1, data$SRP25nRev)
table(data$SRP25nRev)

 1  2  3  4  5 
 9 16 37 35  9 
table(data$SRP_36n)

 1  2  3  4  5 
 8 21 15 25 37 
data$SRP36nRev = data$SRP_36n
data$SRP36nRev = ifelse(data$SRP_36n == 1, 5, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 2, 4, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 4, 2, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 5, 1, data$SRP36nRev)
table(data$SRP36nRev)

 1  2  3  4  5 
37 25 15 21  8 
table(data$SRP_47n)

 1  2  3  4  5 
 8 54 26 13  5 
data$SRP47nRev = data$SRP_47n
data$SRP47nRev = ifelse(data$SRP_47n == 1, 5, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 2, 4, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 4, 2, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 5, 1, data$SRP47nRev)
table(data$SRP47nRev)

 1  2  3  4  5 
 5 13 26 54  8 
# ASB

table(data$SRP_05n)

 1  2  3  4  5 
15  8  2 16 65 
data$SRP5nRev = data$SRP_05n
data$SRP5nRev = ifelse(data$SRP_05n == 1, 5, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 2, 4, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 4, 2, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 5, 1, data$SRP5nRev)
table(data$SRP5nRev)

 1  2  3  4  5 
65 16  2  8 15 
table(data$SRP_06n)

 1  2  4  5 
10  4 17 75 
data$SRP6nRev = data$SRP_06n
data$SRP6nRev = ifelse(data$SRP_06n == 1, 5, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 2, 4, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 4, 2, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 5, 1, data$SRP6nRev)
table(data$SRP6nRev)

 1  2  4  5 
75 17  4 10 
table(data$SRP_18n)

 1  2  4  5 
 6  1 12 87 
data$SRP18nRev = data$SRP_18n
data$SRP18nRev = ifelse(data$SRP_18n == 1, 5, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 2, 4, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 4, 2, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 5, 1, data$SRP18nRev)
table(data$SRP18nRev)

 1  2  4  5 
87 12  1  6 
table(data$SRP_21n)

 1  2  3  4  5 
 6 13  8 24 55 
data$SRP21nRev = data$SRP_21n
data$SRP21nRev = ifelse(data$SRP_21n == 1, 5, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 2, 4, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 4, 2, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 5, 1, data$SRP21nRev)
table(data$SRP21nRev)

 1  2  3  4  5 
55 24  8 13  6 
table(data$SRP_34n)

 1  2  3  4  5 
 3  4  1 13 85 
data$SRP34nRev = data$SRP_34n
data$SRP34nRev = ifelse(data$SRP_34n == 1, 5, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 2, 4, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 4, 2, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 5, 1, data$SRP34nRev)
table(data$SRP34nRev)

 1  2  3  4  5 
85 13  1  4  3 
table(data$SRP_46n)

 1  2  3  4  5 
18 21  2 16 49 
data$SRP46nRev = data$SRP_46n
data$SRP46nRev = ifelse(data$SRP_46n == 1, 5, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 2, 4, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 4, 2, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 5, 1, data$SRP46nRev)
table(data$SRP46nRev)

 1  2  3  4  5 
49 16  2 21 18 

Levenson Reverse Codes

(3, 7, 10, 13, 15, 21, 26)

Bold missing from half of surveys

See code book to match the questions in figure to the numeric values in survey.

Citation: Levenson, M. R., Kiehl, K. A., & Fitzpatrick, C. M. (1995). Assessing psychopathic attributes in a noninstitutionalized population. Journal of Personality and Social Psychology, 68(1), 151–158.

table(data$Lev_10n)

 1  2  3  4 
 5 23 52 26 
data$Lev_10nRev = data$Lev_10n
data$Lev_10nRev = ifelse(data$Lev_10n == 1, 4, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 2, 3, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 3, 2, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 4, 1, data$Lev_10nRev)
table(data$Lev_10nRev)

 1  2  3  4 
26 52 23  5 
table(data$Lev_13n)

 1  2  3  4 
 3  9 41 53 
data$Lev_13nRev = data$Lev_12n
data$Lev_13nRev = ifelse(data$Lev_13n == 1, 4, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 2, 3, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 3, 2, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 4, 1, data$Lev_13nRev)
table(data$Lev_13nRev)

 1  2  3  4 
53 41  9  3 
table(data$Lev_15n)

 1  2  3  4 
 3  8 32 17 
data$Lev_15nRev = data$Lev_15n
data$Lev_15nRev = ifelse(data$Lev_15n == 1, 4, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 2, 3, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 3, 2, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 4, 1, data$Lev_15nRev)
table(data$Lev_15nRev)

 1  2  3  4 
17 32  8  3 
table(data$Lev_21n)

 1  2  3  4 
 1  6 46 53 
data$Lev_21nRev = data$Lev_21n
data$Lev_21nRev = ifelse(data$Lev_21n == 1, 4, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 2, 3, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 3, 2, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 4, 1, data$Lev_21nRev)
table(data$Lev_21nRev)

 1  2  3  4 
53 46  6  1 
table(data$Lev_26n)

 1  2  3  4 
 6  3 54 43 
data$Lev_26nRev = data$Lev_26n
data$Lev_26nRev = ifelse(data$Lev_26n == 1, 4, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 2, 3, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 3, 2, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 4, 1, data$Lev_26nRev)
table(data$Lev_26nRev)

 1  2  3  4 
43 54  3  6 
table(data$Lev_03n)

 1  2  3  4 
 1 12 56 37 
data$Lev_03nRev = data$Lev_03n
data$Lev_03nRev = ifelse(data$Lev_03n == 1, 4, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 2, 3, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 3, 2, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 4, 1, data$Lev_03nRev)
table(data$Lev_03nRev)

 1  2  3  4 
37 56 12  1 
table(data$Lev_07n)

 1  2  3  4 
 1 24 56 24 
data$Lev_07nRev = data$Lev_07n
data$Lev_07nRev = ifelse(data$Lev_07n == 1, 4, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 2, 3, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 3, 2, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 4, 1, data$Lev_07nRev)
table(data$Lev_07nRev)

 1  2  3  4 
24 56 24  1 

ICU

(1, 3, 5, 8, 13, 14, 15, 16, 17, 19, 23, 24)

Essau, C. A., Sasagawa, S., & Frick, P. J. (2006). Callous-unemotional traits in a community sample of adolescents. Assessment, 13(4), 454-469.

Code Reference

# Callous

table(data$ICU_8n)

 2  3  4 
16 56 34 
data$ICU_8nRev = data$ICU_8n
data$ICU_8nRev = ifelse(data$ICU_8n == 1, 4, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 2, 3, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 3, 2, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 4, 1, data$ICU_8nRev)
table(data$ICU_8nRev)

 1  2  3 
34 56 16 
# Uncaring 

table(data$ICU_15n)

 1  2  3  4 
 1 13 41 51 
data$ICU_15nRev = data$ICU_15n
data$ICU_15nRev = ifelse(data$ICU_15n == 1, 4, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 2, 3, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 3, 2, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 4, 1, data$ICU_15nRev)
table(data$ICU_15nRev)

 1  2  3  4 
51 41 13  1 
table(data$ICU_23n)

 1  2  3  4 
 1 16 40 49 
data$ICU_23nRev = data$ICU_23n
data$ICU_23nRev = ifelse(data$ICU_23n == 1, 4, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 2, 3, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 3, 2, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 4, 1, data$ICU_23nRev)
table(data$ICU_23nRev)

 1  2  3  4 
49 40 16  1 
table(data$ICU_16n)

 2  3  4 
11 43 52 
data$ICU_16nRev = data$ICU_16n
data$ICU_16nRev = ifelse(data$ICU_16n == 1, 4, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 2, 3, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 3, 2, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 4, 1, data$ICU_16nRev)
table(data$ICU_16nRev)

 1  2  3 
52 43 11 
table(data$ICU_3n)

 1  2  3  4 
 1  3 29 71 
data$ICU_3nRev = data$ICU_3n
data$ICU_3nRev = ifelse(data$ICU_3n == 1, 4, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 2, 3, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 3, 2, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 4, 1, data$ICU_3nRev)
table(data$ICU_3nRev)

 1  2  3  4 
71 29  3  1 
table(data$ICU_17n)

 2  3  4 
 6 45 55 
data$ICU_17nRev = data$ICU_17n
data$ICU_17nRev = ifelse(data$ICU_17n == 1, 4, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 2, 3, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 3, 2, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 4, 1, data$ICU_17nRev)
table(data$ICU_17nRev)

 1  2  3 
55 45  6 
table(data$ICU_24n)

 1  2  3  4 
 6 25 44 31 
data$ICU_24nRev = data$ICU_24n
data$ICU_24nRev = ifelse(data$ICU_24n == 1, 4, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 2, 3, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 3, 2, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 4, 1, data$ICU_24nRev)
table(data$ICU_24nRev)

 1  2  3  4 
31 44 25  6 
table(data$ICU_13n)

 1  2  3  4 
 7 46 43 10 
data$ICU_13nRev = data$ICU_13n
data$ICU_13nRev = ifelse(data$ICU_13n == 1, 4, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 2, 3, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 3, 2, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 4, 1, data$ICU_13nRev)
table(data$ICU_13nRev)

 1  2  3  4 
10 43 46  7 
table(data$ICU_5n)

 1  2  3  4 
 4 16 42 44 
data$ICU_5nRev = data$ICU_5n
data$ICU_5nRev = ifelse(data$ICU_5n == 1, 4, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 2, 3, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 3, 2, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 4, 1, data$ICU_5nRev)
table(data$ICU_5nRev)

 1  2  3  4 
44 42 16  4 
# Unemotional 

table(data$ICU_1n)

 1  2  3  4 
24 48 23 11 
data$ICU_1nRev = data$ICU_1n
data$ICU_1nRev = ifelse(data$ICU_1n == 1, 4, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 2, 3, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 3, 2, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 4, 1, data$ICU_1nRev)
table(data$ICU_1nRev)

 1  2  3  4 
11 23 48 24 
table(data$ICU_19n)

 1  2  3  4 
29 35 26 16 
data$ICU_19nRev = data$ICU_19n
data$ICU_19nRev = ifelse(data$ICU_19n == 1, 4, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 2, 3, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 3, 2, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 4, 1, data$ICU_19nRev)
table(data$ICU_19nRev)

 1  2  3  4 
16 26 35 29 
table(data$ICU_14n)

 1  2  3  4 
26 47 24  9 
data$ICU_14nRev = data$ICU_14n
data$ICU_14nRev = ifelse(data$ICU_14n == 1, 4, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 2, 3, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 3, 2, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 4, 1, data$ICU_14nRev)
table(data$ICU_14nRev)

 1  2  3  4 
 9 24 47 26 

SSS

(1, 29, 32, 36, 5, 8, 24, 34, 39, 3, 16, 17, 28, 6, 9, 14, 18, 22)

Recoding was done by creating a “false object” or a place holder since it is a binary scale. Example below.

Diagram of recode

\[ A (OriginalValue) -> C(Placeholder) \] \[ B(OrginalValue) -> A(ReversedValue) \]

\[ C(Placeholder) -> B(ReverseValue) \]

Code Reference

# Disinhibition 


table(data$ZSSS_1n)

 0  1 
22 84 
data$ZSSS_1nRevFalse <- data$ZSSS_1n
data$ZSSS_1nRevFalse = ifelse(data$ZSSS_1n == 0, 2, data$ZSSS_1nRevFalse)
data$ZSSS_1nRevFalse = ifelse(data$ZSSS_1n == 1, 0, data$ZSSS_1nRevFalse)
table(data$ZSSS_1nRevFalse)

 0  2 
84 22 
data$ZSSS_1nRev <- data$ZSSS_1nRevFalse
data$ZSSS_1nRev <- ifelse(data$ZSSS_1nRevFalse == 2, 1, data$ZSSS_1nRev)
table(data$ZSSS_1nRev)

 0  1 
84 22 
table(data$ZSSS_29n)

 0  1 
19 86 
data$ZSSS_29nRevFalse <- data$ZSSS_29n
data$ZSSS_29nRevFalse = ifelse(data$ZSSS_29n == 0, 2, data$ZSSS_29nRevFalse)
data$ZSSS_29nRevFalse = ifelse(data$ZSSS_29n == 1, 0, data$ZSSS_29nRevFalse)
table(data$ZSSS_29nRevFalse)

 0  2 
86 19 
data$ZSSS_29nRev <- data$ZSSS_29nRevFalse
data$ZSSS_29nRev <- ifelse(data$ZSSS_29nRevFalse == 2, 1, data$ZSSS_29nRev)
table(data$ZSSS_29nRev)

 0  1 
86 19 
table(data$ZSSS_32n)

 0  1 
65 41 
data$ZSSS_32nRevFalse <- data$ZSSS_32n
data$ZSSS_32nRevFalse = ifelse(data$ZSSS_32n == 0, 2, data$ZSSS_32nRevFalse)
data$ZSSS_32nRevFalse = ifelse(data$ZSSS_32n == 1, 0, data$ZSSS_32nRevFalse)
table(data$ZSSS_32nRevFalse)

 0  2 
41 65 
data$ZSSS_32nRev <- data$ZSSS_32nRevFalse
data$ZSSS_32nRev <- ifelse(data$ZSSS_32nRevFalse == 2, 1, data$ZSSS_32nRev)
table(data$ZSSS_32nRev)

 0  1 
41 65 
table(data$ZSSS_36n)

 0  1 
41 63 
data$ZSSS_36nRevFalse <- data$ZSSS_36n
data$ZSSS_36nRevFalse = ifelse(data$ZSSS_36n == 0, 2, data$ZSSS_36nRevFalse)
data$ZSSS_36nRevFalse = ifelse(data$ZSSS_36n == 1, 0, data$ZSSS_36nRevFalse)
table(data$ZSSS_36nRevFalse)

 0  2 
63 41 
data$ZSSS_36nRev <- data$ZSSS_36nRevFalse
data$ZSSS_36nRev <- ifelse(data$ZSSS_36nRevFalse == 2, 1, data$ZSSS_36nRev)
table(data$ZSSS_36nRev)

 0  1 
63 41 
# Boredom 



table(data$ZSSS_5n)

 0  1 
12 94 
data$ZSSS_5nRevFalse <- data$ZSSS_5n
data$ZSSS_5nRevFalse = ifelse(data$ZSSS_5n == 0, 2, data$ZSSS_5nRevFalse)
data$ZSSS_5nRevFalse = ifelse(data$ZSSS_5n == 1, 0, data$ZSSS_5nRevFalse)
table(data$ZSSS_5nRevFalse)

 0  2 
94 12 
data$ZSSS_5nRev <- data$ZSSS_5nRevFalse
data$ZSSS_5nRev <- ifelse(data$ZSSS_5nRevFalse == 2, 1, data$ZSSS_5nRev)
table(data$ZSSS_5nRev)

 0  1 
94 12 
table(data$ZSSS_8n)

 0  1 
31 75 
data$ZSSS_8nRevFalse <- data$ZSSS_8n
data$ZSSS_8nRevFalse = ifelse(data$ZSSS_8n == 0, 2, data$ZSSS_8nRevFalse)
data$ZSSS_8nRevFalse = ifelse(data$ZSSS_8n == 1, 0, data$ZSSS_8nRevFalse)
table(data$ZSSS_8nRevFalse)

 0  2 
75 31 
data$ZSSS_8nRev <- data$ZSSS_8nRevFalse
data$ZSSS_8nRev <- ifelse(data$ZSSS_8nRevFalse == 2, 1, data$ZSSS_8nRev)
table(data$ZSSS_8nRev)

 0  1 
75 31 
table(data$ZSSS_24n)

 0  1 
24 82 
data$ZSSS_24nRevFalse <- data$ZSSS_24n
data$ZSSS_24nRevFalse = ifelse(data$ZSSS_24n == 0, 2, data$ZSSS_24nRevFalse)
data$ZSSS_24nRevFalse = ifelse(data$ZSSS_24n == 1, 0, data$ZSSS_24nRevFalse)
table(data$ZSSS_24nRevFalse)

 0  2 
82 24 
data$ZSSS_24nRev <- data$ZSSS_24nRevFalse
data$ZSSS_24nRev <- ifelse(data$ZSSS_24nRevFalse == 2, 1, data$ZSSS_24nRev)
table(data$ZSSS_24nRev)

 0  1 
82 24 
table(data$ZSSS_34n)

 0  1 
33 73 
data$ZSSS_34nRevFalse <- data$ZSSS_34n
data$ZSSS_34nRevFalse = ifelse(data$ZSSS_34n == 0, 2, data$ZSSS_34nRevFalse)
data$ZSSS_34nRevFalse = ifelse(data$ZSSS_34n == 1, 0, data$ZSSS_34nRevFalse)
table(data$ZSSS_34nRevFalse)

 0  2 
73 33 
data$ZSSS_34nRev <- data$ZSSS_34nRevFalse
data$ZSSS_34nRev <- ifelse(data$ZSSS_34nRevFalse == 2, 1, data$ZSSS_34nRev)
table(data$ZSSS_34nRev)

 0  1 
73 33 
table(data$ZSSS_39n)

 0  1 
26 80 
data$ZSSS_39nRevFalse <- data$ZSSS_39n
data$ZSSS_39nRevFalse = ifelse(data$ZSSS_39n == 0, 2, data$ZSSS_39nRevFalse)
data$ZSSS_39nRevFalse = ifelse(data$ZSSS_39n == 1, 0, data$ZSSS_39nRevFalse)
table(data$ZSSS_39nRevFalse)

 0  2 
80 26 
data$ZSSS_39nRev <- data$ZSSS_39nRevFalse
data$ZSSS_39nRev <- ifelse(data$ZSSS_39nRevFalse == 2, 1, data$ZSSS_39nRev)
table(data$ZSSS_39nRev)

 0  1 
80 26 
# Thrill 


table(data$ZSSS_3n)

 0  1 
61 45 
data$ZSSS_3nRevFalse <- data$ZSSS_3n
data$ZSSS_3nRevFalse = ifelse(data$ZSSS_3n == 0, 2, data$ZSSS_3nRevFalse)
data$ZSSS_3nRevFalse = ifelse(data$ZSSS_3n == 1, 0, data$ZSSS_3nRevFalse)
table(data$ZSSS_3nRevFalse)

 0  2 
45 61 
data$ZSSS_3nRev <- data$ZSSS_3nRevFalse
data$ZSSS_3nRev <- ifelse(data$ZSSS_3nRevFalse == 2, 1, data$ZSSS_3nRev)
table(data$ZSSS_3nRev)

 0  1 
45 61 
table(data$ZSSS_16n)

 0  1 
67 39 
data$ZSSS_16nRevFalse <- data$ZSSS_16n
data$ZSSS_16nRevFalse = ifelse(data$ZSSS_16n == 0, 2, data$ZSSS_16nRevFalse)
data$ZSSS_16nRevFalse = ifelse(data$ZSSS_16n == 1, 0, data$ZSSS_16nRevFalse)
table(data$ZSSS_16nRevFalse)

 0  2 
39 67 
data$ZSSS_16nRev <- data$ZSSS_16nRevFalse
data$ZSSS_16nRev <- ifelse(data$ZSSS_16nRevFalse == 2, 1, data$ZSSS_16nRev)
table(data$ZSSS_16nRev)

 0  1 
39 67 
table(data$ZSSS_17n)

 0  1 
80 26 
data$ZSSS_17nRevFalse <- data$ZSSS_17n
data$ZSSS_17nRevFalse = ifelse(data$ZSSS_17n == 0, 2, data$ZSSS_17nRevFalse)
data$ZSSS_17nRevFalse = ifelse(data$ZSSS_17n == 1, 0, data$ZSSS_17nRevFalse)
table(data$ZSSS_17nRevFalse)

 0  2 
26 80 
data$ZSSS_17nRev <- data$ZSSS_17nRevFalse
data$ZSSS_17nRev <- ifelse(data$ZSSS_17nRevFalse == 2, 1, data$ZSSS_17nRev)
table(data$ZSSS_17nRev)

 0  1 
26 80 
table(data$ZSSS_23n)

 0  1 
70 36 
data$ZSSS_23nRevFalse <- data$ZSSS_23n
data$ZSSS_23nRevFalse = ifelse(data$ZSSS_23n == 0, 2, data$ZSSS_23nRevFalse)
data$ZSSS_23nRevFalse = ifelse(data$ZSSS_23n == 1, 0, data$ZSSS_23nRevFalse)
table(data$ZSSS_23nRevFalse)

 0  2 
36 70 
data$ZSSS_23nRev <- data$ZSSS_23nRevFalse
data$ZSSS_23nRev <- ifelse(data$ZSSS_23nRevFalse == 2, 1, data$ZSSS_23nRev)
table(data$ZSSS_23nRev)

 0  1 
36 70 
table(data$ZSSS_28n)

 0  1 
45 60 
data$ZSSS_28nRevFalse <- data$ZSSS_28n
data$ZSSS_28nRevFalse = ifelse(data$ZSSS_28n == 0, 2, data$ZSSS_28nRevFalse)
data$ZSSS_28nRevFalse = ifelse(data$ZSSS_28n == 1, 0, data$ZSSS_28nRevFalse)
table(data$ZSSS_28nRevFalse)

 0  2 
60 45 
data$ZSSS_28nRev <- data$ZSSS_28nRevFalse
data$ZSSS_28nRev <- ifelse(data$ZSSS_28nRevFalse == 2, 1, data$ZSSS_28nRev)
table(data$ZSSS_28nRev)

 0  1 
60 45 
# Exp 


table(data$ZSSS_6n)

 0  1 
61 45 
data$ZSSS_6nRevFalse <- data$ZSSS_6n
data$ZSSS_6nRevFalse = ifelse(data$ZSSS_6n == 0, 2, data$ZSSS_6nRevFalse)
data$ZSSS_6nRevFalse = ifelse(data$ZSSS_6n == 1, 0, data$ZSSS_6nRevFalse)
table(data$ZSSS_6nRevFalse)

 0  2 
45 61 
data$ZSSS_6nRev <- data$ZSSS_6nRevFalse
data$ZSSS_6nRev <- ifelse(data$ZSSS_6nRevFalse == 2, 1, data$ZSSS_6nRev)
table(data$ZSSS_6nRev)

 0  1 
45 61 
table(data$ZSSS_9n)

 0  1 
61 45 
data$ZSSS_9nRevFalse <- data$ZSSS_9n
data$ZSSS_9nRevFalse = ifelse(data$ZSSS_9n == 0, 2, data$ZSSS_9nRevFalse)
data$ZSSS_9nRevFalse = ifelse(data$ZSSS_9n == 1, 0, data$ZSSS_9nRevFalse)
table(data$ZSSS_9nRevFalse)

 0  2 
45 61 
data$ZSSS_9nRev <- data$ZSSS_9nRevFalse
data$ZSSS_9nRev <- ifelse(data$ZSSS_9nRevFalse == 2, 1, data$ZSSS_9nRev)
table(data$ZSSS_9nRev)

 0  1 
45 61 
table(data$ZSSS_14n)

 0  1 
59 47 
data$ZSSS_14nRevFalse <- data$ZSSS_14n
data$ZSSS_14nRevFalse = ifelse(data$ZSSS_14n == 0, 2, data$ZSSS_14nRevFalse)
data$ZSSS_14nRevFalse = ifelse(data$ZSSS_14n == 1, 0, data$ZSSS_14nRevFalse)
table(data$ZSSS_14nRevFalse)

 0  2 
47 59 
data$ZSSS_14nRev <- data$ZSSS_14nRevFalse
data$ZSSS_14nRev <- ifelse(data$ZSSS_14nRevFalse == 2, 1, data$ZSSS_14nRev)
table(data$ZSSS_14nRev)

 0  1 
47 59 
table(data$ZSSS_18n)

 0  1 
51 55 
data$ZSSS_18nRevFalse <- data$ZSSS_18n
data$ZSSS_18nRevFalse = ifelse(data$ZSSS_18n == 0, 2, data$ZSSS_18nRevFalse)
data$ZSSS_18nRevFalse = ifelse(data$ZSSS_18n == 1, 0, data$ZSSS_18nRevFalse)
table(data$ZSSS_18nRevFalse)

 0  2 
55 51 
data$ZSSS_18nRev <- data$ZSSS_18nRevFalse
data$ZSSS_18nRev <- ifelse(data$ZSSS_18nRevFalse == 2, 1, data$ZSSS_18nRev)
table(data$ZSSS_18nRev)

 0  1 
55 51 
table(data$ZSSS_22n)

 0  1 
91 14 
data$ZSSS_22nRevFalse <- data$ZSSS_22n
data$ZSSS_22nRevFalse = ifelse(data$ZSSS_22n == 0, 2, data$ZSSS_22nRevFalse)
data$ZSSS_22nRevFalse = ifelse(data$ZSSS_22n == 1, 0, data$ZSSS_22nRevFalse)
table(data$ZSSS_22nRevFalse)

 0  2 
14 91 
data$ZSSS_22nRev <- data$ZSSS_22nRevFalse
data$ZSSS_22nRev <- ifelse(data$ZSSS_22nRevFalse == 2, 1, data$ZSSS_22nRev)
table(data$ZSSS_22nRev)

 0  1 
14 91 

Scales

SRP

# SRP Tot

data$SRPTotalScore <- (data$SRP_01n + data$SRP_02n + data$SRP_03n + data$SRP_04n +  data$SRP5nRev + data$SRP6nRev + data$SRP_07n + 
                  data$SRP_08n + data$SRP_09n + data$SRP_10n + data$SRP11nRev + data$SRP_12n + data$SRP_13n + data$SRP14nRev +
                  data$SRP_15n + data$SRP16nRev + data$SRP_17n + data$SRP18nRev + data$SRP19nRev + data$SRP_20n + data$SRP21nRev +
                  data$SRP22nRev + data$SRP23nRev + data$SRP24nRev + data$SRP25nRev + data$SRP26nRev + data$SRP_27n + data$SRP_28n + 
                  data$SRP_29n + data$SRP_30n + data$SRP31nRev + data$SRP_32n + data$SRP_33n + data$SRP34nRev +  data$SRP_35n + 
                  data$SRP36nRev +  data$SRP_37n + data$SRP38nRev + data$SRP_39n + data$SRP_40n + data$SRP_41n + data$SRP_42n + 
                  data$SRP_43n + data$SRP44nRev + data$SRP_45n + data$SRP46nRev + data$SRP47nRev + data$SRP_48n + data$SRP_49n + 
                  data$SRP_50n + data$SRP_51n + data$SRP_52n + data$SRP_53n + data$SRP_54n + data$SRP_55n +  data$SRP_56n +
                  data$SRP_57n + data$SRP_58n + data$SRP_59n + data$SRP_60n + data$SRP61nRev + data$SRP_62n + data$SRP_63n + data$SRP_64n)



#SRP IPM 

data$SRPIPMTotal <- (data$SRP_03n + data$SRP_08n + data$SRP_13n + data$SRP16nRev + data$SRP_20n + data$SRP24nRev + data$SRP_27n + data$SRP31nRev + 
                  data$SRP_35n + data$SRP38nRev + data$SRP_41n + data$SRP_45n + data$SRP_50n + data$SRP_54n + data$SRP_58n + data$SRP61nRev)



# SRP Callous 

data$SRPCATotal <- (data$SRP_02n + data$SRP_07n + data$SRP11nRev + data$SRP_15n + data$SRP19nRev + data$SRP23nRev + data$SRP26nRev + data$SRP_30n + data$SRP_33n +  data$SRP_37n + data$SRP_40n + data$SRP44nRev + data$SRP_48n + data$SRP_53n + data$SRP_56n + data$SRP_60n)





#SRP lifestyle 

data$SRPELSTotal <- (data$SRP_01n + data$SRP_04n + data$SRP_09n + data$SRP14nRev + data$SRP_17n + data$SRP22nRev + data$SRP25nRev + data$SRP_28n + data$SRP_32n + data$SRP36nRev +  data$SRP_39n + data$SRP_42n + data$SRP47nRev + data$SRP_51n +data$SRP_55n + data$SRP_59n)


# SRP Antisocial 


data$SRPASBTotal <-  (data$SRP5nRev + data$SRP6nRev + data$SRP_10n + data$SRP_12n + data$SRP18nRev + data$SRP21nRev + data$SRP_29n + data$SRP34nRev + data$SRP_43n + data$SRP46nRev + data$SRP_49n + data$SRP_52n + data$SRP_57n + data$SRP_62n + data$SRP_63n + data$SRP_64n)

ICU

# ICU total 

data$ICUTotScore <- (data$ICU_1nRev + data$ICU_2n + data$ICU_3nRev + data$ICU_4n + data$ICU_5nRev + data$ICU_6n + 
                  data$ICU_7n + data$ICU_8nRev + data$ICU_9n + data$ICU_10n + data$ICU_11n + data$ICU_12n + data$ICU_13nRev +
                  data$ICU_14nRev + data$ICU_15nRev + data$ICU_16nRev + data$ICU_17nRev + data$ICU_18n + data$ICU_19nRev +
                  data$ICU_20n + data$ICU_21n + data$ICU_22n + data$ICU_23nRev + data$ICU_24nRev)


# ICU Cal 

data$ICUCalTotalScore <- (data$ICU_4n + data$ICU_8nRev + data$ICU_9n + data$ICU_18n + data$ICU_11n +  data$ICU_21n + data$ICU_7n + data$ICU_20n +
                  data$ICU_2n + data$ICU_12n + data$ICU_10n)





# ICU Uncare

data$ICUUncareTotalScore <- (data$ICU_15nRev + data$ICU_23nRev + data$ICU_16nRev + data$ICU_3nRev + data$ICU_17nRev + data$ICU_24nRev +
                     data$ICU_13nRev + data$ICU_5nRev)




# ICU  Unemo 


data$ICUUnemoTotal <- (data$ICU_1nRev + data$ICU_19nRev + data$ICU_6n + data$ICU_22n + data$ICU_14nRev)

LSRP

# Total

data$LevTotalScore <- (data$Lev_01n + data$Lev_02n + data$Lev_03nRev + data$Lev_04n + data$Lev_05n + data$Lev_06n + data$Lev_07nRev + data$Lev_08n +  data$Lev_09n + data$Lev_10nRev + data$Lev_11n + data$Lev_12n + data$Lev_13nRev + data$Lev_16n + data$Lev_17n + data$Lev_18n + data$Lev_19n + data$Lev_20n + data$Lev_21nRev + data$Lev_22n + data$Lev_23n + data$Lev_24n + data$Lev_25n + data$Lev_26nRev)



# Primary 


data$LevPrimTotalScore <- (data$Lev_02n + data$Lev_04n + data$Lev_07nRev + data$Lev_09n + data$Lev_11n + data$Lev_12n + data$Lev_13nRev +
                   data$Lev_17n + data$Lev_19n +  data$Lev_21nRev + data$Lev_22n + data$Lev_23n + data$Lev_24n + data$Lev_25n + data$Lev_26nRev)






# Seconnday 

data$LevSecTotalScore <- (data$Lev_01n + data$Lev_03nRev + data$Lev_05n + data$Lev_06n + data$Lev_08n + data$Lev_10nRev + data$Lev_16n + data$Lev_18n + data$Lev_20n)

ZSSS

# Total 

data$SSSTotalScore <-  (data$ZSSS_1nRev + data$ZSSS_2n + data$ZSSS_3nRev + data$ZSSS_4n + data$ZSSS_5nRev + data$ZSSS_6nRev + data$ZSSS_7n + data$ZSSS_8nRev + data$ZSSS_9nRev + data$ZSSS_10n + data$ZSSS_11n + data$ZSSS_12n + data$ZSSS_13n + data$ZSSS_14nRev + data$ZSSS_15n + data$ZSSS_16nRev + data$ZSSS_17nRev + data$ZSSS_18nRev + data$ZSSS_19n + data$ZSSS_20n +  data$ZSSS_21n + data$ZSSS_22nRev + data$ZSSS_23nRev + data$ZSSS_24nRev + data$ZSSS_25n + data$ZSSS_26n + data$ZSSS_27n + data$ZSSS_28nRev + data$ZSSS_29nRev + data$ZSSS_30n + data$ZSSS_31n + data$ZSSS_32nRev + data$ZSSS_33n + data$ZSSS_34nRev + data$ZSSS_35n + data$ZSSS_36nRev + data$ZSSS_37n + data$ZSSS_38n + data$ZSSS_39nRev + data$ZSSS_40n)






# Disinhibited 

data$SSSDISTotal <- (data$ZSSS_12n + data$ZSSS_13n + data$ZSSS_25n + data$ZSSS_30n + data$ZSSS_33n + data$ZSSS_35n +
                  data$ZSSS_1nRev + data$ZSSS_29nRev + data$ZSSS_32nRev + data$ZSSS_36nRev)




# Boredom 

data$SSSBorTotal <- (data$ZSSS_2n + data$ZSSS_7n + data$ZSSS_15n + data$ZSSS_27n + data$ZSSS_31n + data$ZSSS_5nRev + data$ZSSS_8nRev + data$ZSSS_24nRev +
                  data$ZSSS_34nRev + data$ZSSS_39nRev)




# Thrill 
data$SSSThrilTotal <- (data$ZSSS_11n + data$ZSSS_20n + data$ZSSS_21n + data$ZSSS_38n + data$ZSSS_40n + data$ZSSS_3nRev +
                    data$ZSSS_16nRev + data$ZSSS_17nRev + data$ZSSS_23nRev + data$ZSSS_28nRev)




# Exp 

data$SSSExpTotal <- (data$ZSSS_4n + data$ZSSS_10n + data$ZSSS_19n + data$ZSSS_26n + data$ZSSS_37n + data$ZSSS_6nRev +
                  data$ZSSS_9nRev + data$ZSSS_14nRev + data$ZSSS_18nRev + data$ZSSS_22nRev) 

Autonomic Measures

Resting Heart Rate

data$HRbaseline <- (data$HRT_00_00 +  data$HRT_00_01 +  data$HRT_00_02 + data$HRT_00_03 + data$HRT_00_04 +  data$HRT_00_05 +  data$HRT_00_06 +  data$HRT_00_07 + data$HRT_00_08 +  data$HRT_00_09 + data$HRT_00_10 + data$HRT_00_11 + data$HRT_00_12 +  data$HRT_00_13 +  data$HRT_00_14 + data$HRT_00_15 + data$HRT_00_16 +  data$HRT_00_17 +  data$HRT_00_18 + data$HRT_00_19 + data$HRT_00_20 +  data$HRT_00_21 +  data$HRT_00_22 + data$HRT_00_23 + data$HRT_00_24 +  data$HRT_00_25 + data$HRT_00_26 +  data$HRT_00_27 + data$HRT_00_28 +  data$HRT_00_29 +  data$HRT_00_30 +  data$HRT_00_31 + data$HRT_00_32 +  data$HRT_00_33 +  data$HRT_00_34 + data$HRT_00_35 + data$HRT_00_36 + data$HRT_00_37 +  data$HRT_00_38 +  data$HRT_00_39 + data$HRT_00_40 +  data$HRT_00_41 +  data$HRT_00_42 + data$HRT_00_43 + data$HRT_00_44 + data$HRT_00_45 +  data$HRT_00_46 +  data$HRT_00_47 +  data$HRT_00_48 + data$HRT_00_49 + data$HRT_00_50 +  data$HRT_00_51 +  data$HRT_00_52 +  data$HRT_00_53 + data$HRT_00_54 + data$HRT_00_55 + data$HRT_00_56 +  data$HRT_00_57   +  data$HRT_00_58 + data$HRT_00_59 +  data$HRT_01_00 +  data$HRT_01_01 +  data$HRT_01_02 + data$HRT_01_03 + data$HRT_01_04 + data$HRT_01_05 +  data$HRT_01_06 +  data$HRT_01_07 + data$HRT_01_08 +  data$HRT_01_09 +  data$HRT_01_10 +  data$HRT_01_11 + data$HRT_01_12 + data$HRT_01_13 + data$HRT_01_14 +  data$HRT_01_15 +  data$HRT_01_16 +  data$HRT_01_17 +  data$HRT_01_18 + data$HRT_01_19 +  data$HRT_01_20 +  data$HRT_01_21 +  data$HRT_01_22 + data$HRT_01_23 +  data$HRT_01_24 + data$HRT_01_25 +  data$HRT_01_26 +  data$HRT_01_27 + data$HRT_01_28 +  data$HRT_01_29 +  data$HRT_01_30 +  data$HRT_01_31 + data$HRT_01_32 +  data$HRT_01_33 + data$HRT_01_34 +  data$HRT_01_35 +  data$HRT_01_36 + data$HRT_01_37 +  data$HRT_01_38 +  data$HRT_01_39 +  data$HRT_01_40 +  data$HRT_01_41 + data$HRT_01_42 + data$HRT_01_43 + data$HRT_01_44 +  data$HRT_01_45 + data$HRT_01_46 + data$HRT_01_47 +  data$HRT_01_48 +  data$HRT_01_49 +  data$HRT_01_50 +  data$HRT_01_51 + data$HRT_01_52 + data$HRT_01_53 + data$HRT_01_54 +  data$HRT_01_55 +  data$HRT_01_56 +  data$HRT_01_57 + data$HRT_01_58 +  data$HRT_01_59 +  data$HRT_02_00 +  data$HRT_02_01 + data$HRT_02_02 + data$HRT_02_03 +  data$HRT_02_04 +  data$HRT_02_05 +  data$HRT_02_06 +  data$HRT_02_07 + data$HRT_02_08 + data$HRT_02_09 +  data$HRT_02_10 +  data$HRT_02_11 +  data$HRT_02_12 + data$HRT_02_13 +  data$HRT_02_14 +  data$HRT_02_15 + data$HRT_02_16 +  data$HRT_02_17 + data$HRT_02_18 +  data$HRT_02_19 + data$HRT_02_20 +  data$HRT_02_21 +  data$HRT_02_22 + data$HRT_02_23 +  data$HRT_02_24 + data$HRT_02_25 +  data$HRT_02_26 +  data$HRT_02_27 +  data$HRT_02_28 +  data$HRT_02_29 +  data$HRT_02_30 +  data$HRT_02_31   + data$HRT_02_32 + data$HRT_02_33 +  data$HRT_02_34 +  data$HRT_02_35 +  data$HRT_02_36 +  data$HRT_02_37 + data$HRT_02_38 +  data$HRT_02_39 +  data$HRT_02_40 + data$HRT_02_41+  data$HRT_02_42 +  data$HRT_02_43 +  data$HRT_02_44 +  data$HRT_02_45 +  data$HRT_02_46 + data$HRT_02_47 +  data$HRT_02_48 + data$HRT_02_49 +  data$HRT_02_50 + data$HRT_02_51 + data$HRT_02_52 +  data$HRT_02_53 +  data$HRT_02_54 +  data$HRT_02_55 + data$HRT_02_56+  data$HRT_02_57 +  data$HRT_02_58 +  data$HRT_02_59)/180

Resting Skin Conductance

data$SCbaseline <- (data$SCT_00_00 + data$SCT_00_01 + data$SCT_00_02 + data$SCT_00_03 + data$SCT_00_04 + data$SCT_00_05 + data$SCT_00_06 + data$SCT_00_07 + data$SCT_00_08 + data$SCT_00_09 + data$SCT_00_10 + data$SCT_00_11 + data$SCT_00_12 + data$SCT_00_13 + data$SCT_00_14 + data$SCT_00_15 + data$SCT_00_16 + data$SCT_00_17 + data$SCT_00_18 + data$SCT_00_19 + data$SCT_00_20 + data$SCT_00_21 + data$SCT_00_22 + data$SCT_00_23 + data$SCT_00_24 + data$SCT_00_25 + data$SCT_00_26 + data$SCT_00_27 + data$SCT_00_28 + data$SCT_00_29 + data$SCT_00_30 + data$SCT_00_31 + data$SCT_00_32 + data$SCT_00_33 + data$SCT_00_34 + data$SCT_00_35 + data$SCT_00_36 + data$SCT_00_37 + data$SCT_00_38 + data$SCT_00_39 + data$SCT_00_40 + data$SCT_00_41 + data$SCT_00_42 + data$SCT_00_43 + data$SCT_00_44 + data$SCT_00_45 + data$SCT_00_46 + data$SCT_00_47 + data$SCT_00_48 + data$SCT_00_49 + data$SCT_00_50 + data$SCT_00_51 + data$SCT_00_52 + data$SCT_00_53 + data$SCT_00_54 + data$SCT_00_55 + data$SCT_00_56 + data$SCT_00_57 + data$SCT_00_58 + data$SCT_00_59 + data$SCT_01_00 + data$SCT_01_01 + data$SCT_01_02 + data$SCT_01_03 + data$SCT_01_04 + data$SCT_01_05 + data$SCT_01_06 + data$SCT_01_07 + data$SCT_01_08 + data$SCT_01_09 + data$SCT_01_10 + data$SCT_01_11 + data$SCT_01_12 + data$SCT_01_13 + data$SCT_01_14 + data$SCT_01_15 + data$SCT_01_16 + data$SCT_01_17 + data$SCT_01_18 + data$SCT_01_19 + data$SCT_01_20 + data$SCT_01_21 + data$SCT_01_22 + data$SCT_01_23 + data$SCT_01_24 + data$SCT_01_25 + data$SCT_01_26 + data$SCT_01_27 + data$SCT_01_28 + data$SCT_01_29 + data$SCT_01_30 + data$SCT_01_31 + data$SCT_01_32 + data$SCT_01_33 + data$SCT_01_34 + data$SCT_01_35 + data$SCT_01_36 + data$SCT_01_37 + data$SCT_01_38 + data$SCT_01_39 + data$SCT_01_40 + data$SCT_01_41 + data$SCT_01_42 + data$SCT_01_43 + data$SCT_01_44 + data$SCT_01_45 + data$SCT_01_46 + data$SCT_01_47 + data$SCT_01_48 + data$SCT_01_49 + data$SCT_01_50 + data$SCT_01_51 + data$SCT_01_52 + data$SCT_01_53 + data$SCT_01_54 + data$SCT_01_55 + data$SCT_01_56 + data$SCT_01_57 + data$SCT_01_58 + data$SCT_01_59 + data$SCT_02_00 + data$SCT_02_01 + data$SCT_02_02 + data$SCT_02_03 + data$SCT_02_04 + data$SCT_02_05 + data$SCT_02_06 + data$SCT_02_07 + data$SCT_02_08 + data$SCT_02_09 + data$SCT_02_10 + data$SCT_02_11 + data$SCT_02_12 + data$SCT_02_13 + data$SCT_02_14 + data$SCT_02_15 + data$SCT_02_16 + data$SCT_02_17 + data$SCT_02_18 + data$SCT_02_19 + data$SCT_02_20 + data$SCT_02_21 + data$SCT_02_22 + data$SCT_02_23 + data$SCT_02_24 + data$SCT_02_25 + data$SCT_02_26 + data$SCT_02_27 + data$SCT_02_28 + data$SCT_02_29 + data$SCT_02_30 + data$SCT_02_31 + data$SCT_02_32 + data$SCT_02_33 + data$SCT_02_34 + data$SCT_02_35 + data$SCT_02_36 + data$SCT_02_37 + data$SCT_02_38 + data$SCT_02_39 + data$SCT_02_40 + data$SCT_02_41 + data$SCT_02_42 + data$SCT_02_43 + data$SCT_02_44 + data$SCT_02_45 + data$SCT_02_46 + data$SCT_02_47 + data$SCT_02_48 + data$SCT_02_49 + data$SCT_02_50 + data$SCT_02_51 + data$SCT_02_52 + data$SCT_02_53 + data$SCT_02_54 + data$SCT_02_55 + data$SCT_02_56 + data$SCT_02_57 + data$SCT_02_58 + data$SCT_02_59)/180

AUC Autonomic

The formula for the AUC code below can be found here.

AUC Social Stressor

HR Combined

data$SSHRCombAUCg <- (data$HrStr_00_01 + data$HrStr_00_00)/2 + (data$HrStr_00_02 + data$HrStr_00_01)/2 + (data$HrStr_00_03 + data$HrStr_00_02)/2 + (data$HrStr_00_04 + data$HrStr_00_03)/2 +
  (data$HrStr_00_05 + data$HrStr_00_04)/2 + (data$HrStr_00_06 + data$HrStr_00_05)/2 + (data$HrStr_00_07 + data$HrStr_00_06)/2 + (data$HrStr_00_08 + data$HrStr_00_07)/2 + (data$HrStr_00_09 + data$HrStr_00_08)/2 + 
  (data$HrStr_00_10 + data$HrStr_00_09)/2 + (data$HrStr_00_11 + data$HrStr_00_10)/2 + (data$HrStr_00_12 + data$HrStr_00_11)/2 + (data$HrStr_00_13 + data$HrStr_00_12)/2 + (data$HrStr_00_14 + data$HrStr_00_13)/2 +
  (data$HrStr_00_15 + data$HrStr_00_14)/2 + (data$HrStr_00_16 + data$HrStr_00_15)/2 + (data$HrStr_00_17 + data$HrStr_00_16)/2 + (data$HrStr_00_18 + data$HrStr_00_17)/2 + (data$HrStr_00_19 + data$HrStr_00_18)/2 +
  (data$HrStr_00_20 + data$HrStr_00_19)/2 + (data$HrStr_00_21 + data$HrStr_00_20)/2 + (data$HrStr_00_22 + data$HrStr_00_21)/2 + (data$HrStr_00_23 + data$HrStr_00_22)/2 + (data$HrStr_00_24 + data$HrStr_00_23)/2 +
  (data$HrStr_00_25 + data$HrStr_00_24)/2 + (data$HrStr_00_26 + data$HrStr_00_25)/2 + (data$HrStr_00_27 + data$HrStr_00_26)/2 + (data$HrStr_00_28 + data$HrStr_00_27)/2 + (data$HrStr_00_29 + data$HrStr_00_28)/2 +
  (data$HrStr_00_30 + data$HrStr_00_29)/2 + (data$HrStr_00_31 + data$HrStr_00_30)/2 + (data$HrStr_00_32 + data$HrStr_00_31)/2 + (data$HrStr_00_33 + data$HrStr_00_32)/2 + (data$HrStr_00_34 + data$HrStr_00_33)/2 +
  (data$HrStr_00_35 + data$HrStr_00_34)/2 + (data$HrStr_00_36 + data$HrStr_00_35)/2 + (data$HrStr_00_37 + data$HrStr_00_36)/2 + (data$HrStr_00_38 + data$HrStr_00_37)/2 + (data$HrStr_00_39 + data$HrStr_00_38)/2 +
  (data$HrStr_00_40 + data$HrStr_00_39)/2 + (data$HrStr_00_41 + data$HrStr_00_40)/2 + (data$HrStr_00_42 + data$HrStr_00_41)/2 + (data$HrStr_00_43 + data$HrStr_00_42)/2 + (data$HrStr_00_44 + data$HrStr_00_43)/2 +
  (data$HrStr_00_45 + data$HrStr_00_44)/2 + (data$HrStr_00_46 + data$HrStr_00_45)/2 + (data$HrStr_00_47 + data$HrStr_00_46)/2 + (data$HrStr_00_48 + data$HrStr_00_47)/2 + (data$HrStr_00_49 + data$HrStr_00_48)/2 +
  (data$HrStr_00_50 + data$HrStr_00_49)/2 + (data$HrStr_00_51 + data$HrStr_00_50)/2 + (data$HrStr_00_52 + data$HrStr_00_51)/2 + (data$HrStr_00_53 + data$HrStr_00_52)/2 + (data$HrStr_00_54 + data$HrStr_00_53)/2 +
  (data$HrStr_00_55 + data$HrStr_00_54)/2 + (data$HrStr_00_56 + data$HrStr_00_55)/2 + (data$HrStr_00_57 + data$HrStr_00_56)/2 + (data$HrStr_00_58 + data$HrStr_00_57)/2 + (data$HrStr_00_59 + data$HrStr_00_58)/2 +
  (data$HrStr_01_00 + data$HrStr_00_59)/2 +
  (data$HrStr_01_01 + data$HrStr_01_00)/2 + (data$HrStr_01_02 + data$HrStr_01_01)/2 + (data$HrStr_01_03 + data$HrStr_01_02)/2 + (data$HrStr_01_04 + data$HrStr_01_03)/2 +
  (data$HrStr_01_05 + data$HrStr_01_04)/2 + (data$HrStr_01_06 + data$HrStr_01_05)/2 + (data$HrStr_01_07 + data$HrStr_01_06)/2 + (data$HrStr_01_08 + data$HrStr_01_07)/2 + (data$HrStr_01_09 + data$HrStr_01_08)/2 + 
  (data$HrStr_01_10 + data$HrStr_01_09)/2 + (data$HrStr_01_11 + data$HrStr_01_10)/2 + (data$HrStr_01_12 + data$HrStr_01_11)/2 + (data$HrStr_01_13 + data$HrStr_01_12)/2 + (data$HrStr_01_14 + data$HrStr_01_13)/2 +
  (data$HrStr_01_15 + data$HrStr_01_14)/2 + (data$HrStr_01_16 + data$HrStr_01_15)/2 + (data$HrStr_01_17 + data$HrStr_01_16)/2 + (data$HrStr_01_18 + data$HrStr_01_17)/2 + (data$HrStr_01_19 + data$HrStr_01_18)/2 +
  (data$HrStr_01_20 + data$HrStr_01_19)/2 + (data$HrStr_01_21 + data$HrStr_01_20)/2 + (data$HrStr_01_22 + data$HrStr_01_21)/2 + (data$HrStr_01_23 + data$HrStr_01_22)/2 + (data$HrStr_01_24 + data$HrStr_01_23)/2 +
  (data$HrStr_01_25 + data$HrStr_01_24)/2 + (data$HrStr_01_26 + data$HrStr_01_25)/2 + (data$HrStr_01_27 + data$HrStr_01_26)/2 + (data$HrStr_01_28 + data$HrStr_01_27)/2 + (data$HrStr_01_29 + data$HrStr_01_28)/2 +
  (data$HrStr_01_30 + data$HrStr_01_29)/2 + (data$HrStr_01_31 + data$HrStr_01_30)/2 + (data$HrStr_01_32 + data$HrStr_01_31)/2 + (data$HrStr_01_33 + data$HrStr_01_32)/2 + (data$HrStr_01_34 + data$HrStr_01_33)/2 +
  (data$HrStr_01_35 + data$HrStr_01_34)/2 + (data$HrStr_01_36 + data$HrStr_01_35)/2 + (data$HrStr_01_37 + data$HrStr_01_36)/2 + (data$HrStr_01_38 + data$HrStr_01_37)/2 + (data$HrStr_01_39 + data$HrStr_01_38)/2 +
  (data$HrStr_01_40 + data$HrStr_01_39)/2 + (data$HrStr_01_41 + data$HrStr_01_40)/2 + (data$HrStr_01_42 + data$HrStr_01_41)/2 + (data$HrStr_01_43 + data$HrStr_01_42)/2 + (data$HrStr_01_44 + data$HrStr_01_43)/2 +
  (data$HrStr_01_45 + data$HrStr_01_44)/2 + (data$HrStr_01_46 + data$HrStr_01_45)/2 + (data$HrStr_01_47 + data$HrStr_01_46)/2 + (data$HrStr_01_48 + data$HrStr_01_47)/2 + (data$HrStr_01_49 + data$HrStr_01_48)/2 +
  (data$HrStr_01_50 + data$HrStr_01_49)/2 + (data$HrStr_01_51 + data$HrStr_01_50)/2 + (data$HrStr_01_52 + data$HrStr_01_51)/2 + (data$HrStr_01_53 + data$HrStr_01_52)/2 + (data$HrStr_01_54 + data$HrStr_01_53)/2 +
  (data$HrStr_01_55 + data$HrStr_01_54)/2 + (data$HrStr_01_56 + data$HrStr_01_55)/2 + (data$HrStr_01_57 + data$HrStr_01_56)/2 + (data$HrStr_01_58 + data$HrStr_01_57)/2 + (data$HrStr_01_59 + data$HrStr_01_58)/2 +
  (data$HrStr_02_00 + data$HrStr_01_59)/2 +
  (data$HrStr_02_01 + data$HrStr_02_00)/2 + (data$HrStr_02_02 + data$HrStr_02_01)/2 + (data$HrStr_02_03 + data$HrStr_02_02)/2 + (data$HrStr_02_04 + data$HrStr_02_03)/2 +
  (data$HrStr_02_05 + data$HrStr_02_04)/2 + (data$HrStr_02_06 + data$HrStr_02_05)/2 + (data$HrStr_02_07 + data$HrStr_02_06)/2 + (data$HrStr_02_08 + data$HrStr_02_07)/2 + (data$HrStr_02_09 + data$HrStr_02_08)/2 + 
  (data$HrStr_02_10 + data$HrStr_02_09)/2 + (data$HrStr_02_11 + data$HrStr_02_10)/2 + (data$HrStr_02_12 + data$HrStr_02_11)/2 + (data$HrStr_02_13 + data$HrStr_02_12)/2 + (data$HrStr_02_14 + data$HrStr_02_13)/2 +
  (data$HrStr_02_15 + data$HrStr_02_14)/2 + (data$HrStr_02_16 + data$HrStr_02_15)/2 + (data$HrStr_02_17 + data$HrStr_02_16)/2 + (data$HrStr_02_18 + data$HrStr_02_17)/2 + (data$HrStr_02_19 + data$HrStr_02_18)/2 +
  (data$HrStr_02_20 + data$HrStr_02_19)/2 + (data$HrStr_02_21 + data$HrStr_02_20)/2 + (data$HrStr_02_22 + data$HrStr_02_21)/2 + (data$HrStr_02_23 + data$HrStr_02_22)/2 + (data$HrStr_02_24 + data$HrStr_02_23)/2 +
  (data$HrStr_02_25 + data$HrStr_02_24)/2 + (data$HrStr_02_26 + data$HrStr_02_25)/2 + (data$HrStr_02_27 + data$HrStr_02_26)/2 + (data$HrStr_02_28 + data$HrStr_02_27)/2 + (data$HrStr_02_29 + data$HrStr_02_28)/2 +
  (data$HrStr_02_30 + data$HrStr_02_29)/2 + (data$HrStr_02_31 + data$HrStr_02_30)/2 + (data$HrStr_02_32 + data$HrStr_02_31)/2 + (data$HrStr_02_33 + data$HrStr_02_32)/2 + (data$HrStr_02_34 + data$HrStr_02_33)/2 +
  (data$HrStr_02_35 + data$HrStr_02_34)/2 + (data$HrStr_02_36 + data$HrStr_02_35)/2 + (data$HrStr_02_37 + data$HrStr_02_36)/2 + (data$HrStr_02_38 + data$HrStr_02_37)/2 + (data$HrStr_02_39 + data$HrStr_02_38)/2 +
  (data$HrStr_02_40 + data$HrStr_02_39)/2 + (data$HrStr_02_41 + data$HrStr_02_40)/2 + (data$HrStr_02_42 + data$HrStr_02_41)/2 + (data$HrStr_02_43 + data$HrStr_02_42)/2 + (data$HrStr_02_44 + data$HrStr_02_43)/2 +
  (data$HrStr_02_45 + data$HrStr_02_44)/2 + (data$HrStr_02_46 + data$HrStr_02_45)/2 + (data$HrStr_02_47 + data$HrStr_02_46)/2 + (data$HrStr_02_48 + data$HrStr_02_47)/2 + (data$HrStr_02_49 + data$HrStr_02_48)/2 +
  (data$HrStr_02_50 + data$HrStr_02_49)/2 + (data$HrStr_02_51 + data$HrStr_02_50)/2 + (data$HrStr_02_52 + data$HrStr_02_51)/2 + (data$HrStr_02_53 + data$HrStr_02_52)/2 + (data$HrStr_02_54 + data$HrStr_02_53)/2 +
  (data$HrStr_02_55 + data$HrStr_02_54)/2 + (data$HrStr_02_56 + data$HrStr_02_55)/2 + (data$HrStr_02_57 + data$HrStr_02_56)/2 + (data$HrStr_02_58 + data$HrStr_02_57)/2 + (data$HrStr_02_59 + data$HrStr_02_58)/2 +
  (data$HrStr_03_01 + data$HrStr_03_00)/2 + (data$HrStr_03_02 + data$HrStr_03_01)/2 + (data$HrStr_03_03 + data$HrStr_03_02)/2 + (data$HrStr_03_04 + data$HrStr_03_03)/2 +
  (data$HrStr_03_05 + data$HrStr_03_04)/2 + (data$HrStr_03_06 + data$HrStr_03_05)/2 + (data$HrStr_03_07 + data$HrStr_03_06)/2 + (data$HrStr_03_08 + data$HrStr_03_07)/2 + (data$HrStr_03_09 + data$HrStr_03_08)/2 + 
  (data$HrStr_03_10 + data$HrStr_03_09)/2 + (data$HrStr_03_11 + data$HrStr_03_10)/2 + (data$HrStr_03_12 + data$HrStr_03_11)/2 + (data$HrStr_03_13 + data$HrStr_03_12)/2 + (data$HrStr_03_14 + data$HrStr_03_13)/2 +
  (data$HrStr_03_15 + data$HrStr_03_14)/2 + (data$HrStr_03_16 + data$HrStr_03_15)/2 + (data$HrStr_03_17 + data$HrStr_03_16)/2 + (data$HrStr_03_18 + data$HrStr_03_17)/2 + (data$HrStr_03_19 + data$HrStr_03_18)/2 +
  (data$HrStr_03_20 + data$HrStr_03_19)/2 + (data$HrStr_03_21 + data$HrStr_03_20)/2 + (data$HrStr_03_22 + data$HrStr_03_21)/2 + (data$HrStr_03_23 + data$HrStr_03_22)/2 + (data$HrStr_03_24 + data$HrStr_03_23)/2 +
  (data$HrStr_03_25 + data$HrStr_03_24)/2 + (data$HrStr_03_26 + data$HrStr_03_25)/2 + (data$HrStr_03_27 + data$HrStr_03_26)/2 + (data$HrStr_03_28 + data$HrStr_03_27)/2 + (data$HrStr_03_29 + data$HrStr_03_28)/2 +
  (data$HrStr_03_30 + data$HrStr_03_29)/2 + (data$HrStr_03_31 + data$HrStr_03_30)/2 + (data$HrStr_03_32 + data$HrStr_03_31)/2 + (data$HrStr_03_33 + data$HrStr_03_32)/2 + (data$HrStr_03_34 + data$HrStr_03_33)/2 +
  (data$HrStr_03_35 + data$HrStr_03_34)/2 + (data$HrStr_03_36 + data$HrStr_03_35)/2 + (data$HrStr_03_37 + data$HrStr_03_36)/2 + (data$HrStr_03_38 + data$HrStr_03_37)/2 + (data$HrStr_03_39 + data$HrStr_03_38)/2 +
  (data$HrStr_03_40 + data$HrStr_03_39)/2 + (data$HrStr_03_41 + data$HrStr_03_40)/2 + (data$HrStr_03_42 + data$HrStr_03_41)/2 + (data$HrStr_03_43 + data$HrStr_03_42)/2 + (data$HrStr_03_44 + data$HrStr_03_43)/2 +
  (data$HrStr_03_45 + data$HrStr_03_44)/2 + (data$HrStr_03_46 + data$HrStr_03_45)/2 + (data$HrStr_03_47 + data$HrStr_03_46)/2 + (data$HrStr_03_48 + data$HrStr_03_47)/2 + (data$HrStr_03_49 + data$HrStr_03_48)/2 +
  (data$HrStr_03_50 + data$HrStr_03_49)/2 + (data$HrStr_03_51 + data$HrStr_03_50)/2 + (data$HrStr_03_52 + data$HrStr_03_51)/2 + (data$HrStr_03_53 + data$HrStr_03_52)/2 + (data$HrStr_03_54 + data$HrStr_03_53)/2 +
  (data$HrStr_03_55 + data$HrStr_03_54)/2 + (data$HrStr_03_56 + data$HrStr_03_55)/2 + (data$HrStr_03_57 + data$HrStr_03_56)/2 + (data$HrStr_03_58 + data$HrStr_03_57)/2 + (data$HrStr_03_59 + data$HrStr_03_58)/2 

data$SSHRCombAUCi <- data$SSHRCombAUCg - (239 * data$HrStr_00_00)

SC Combined

data$SSSCCombAUCg <-(data$ScStr_00_01 + data$ScStr_00_00)/2 + (data$ScStr_00_02 + data$ScStr_00_01)/2 + (data$ScStr_00_03 + data$ScStr_00_02)/2 + (data$ScStr_00_04 + data$ScStr_00_03)/2 +
  (data$ScStr_00_05 + data$ScStr_00_04)/2 + (data$ScStr_00_06 + data$ScStr_00_05)/2 + (data$ScStr_00_07 + data$ScStr_00_06)/2 + (data$ScStr_00_08 + data$ScStr_00_07)/2 + (data$ScStr_00_09 + data$ScStr_00_08)/2 + 
  (data$ScStr_00_10 + data$ScStr_00_09)/2 + (data$ScStr_00_11 + data$ScStr_00_10)/2 + (data$ScStr_00_12 + data$ScStr_00_11)/2 + (data$ScStr_00_13 + data$ScStr_00_12)/2 + (data$ScStr_00_14 + data$ScStr_00_13)/2 +
  (data$ScStr_00_15 + data$ScStr_00_14)/2 + (data$ScStr_00_16 + data$ScStr_00_15)/2 + (data$ScStr_00_17 + data$ScStr_00_16)/2 + (data$ScStr_00_18 + data$ScStr_00_17)/2 + (data$ScStr_00_19 + data$ScStr_00_18)/2 +
  (data$ScStr_00_20 + data$ScStr_00_19)/2 + (data$ScStr_00_21 + data$ScStr_00_20)/2 + (data$ScStr_00_22 + data$ScStr_00_21)/2 + (data$ScStr_00_23 + data$ScStr_00_22)/2 + (data$ScStr_00_24 + data$ScStr_00_23)/2 +
  (data$ScStr_00_25 + data$ScStr_00_24)/2 + (data$ScStr_00_26 + data$ScStr_00_25)/2 + (data$ScStr_00_27 + data$ScStr_00_26)/2 + (data$ScStr_00_28 + data$ScStr_00_27)/2 + (data$ScStr_00_29 + data$ScStr_00_28)/2 +
  (data$ScStr_00_30 + data$ScStr_00_29)/2 + (data$ScStr_00_31 + data$ScStr_00_30)/2 + (data$ScStr_00_32 + data$ScStr_00_31)/2 + (data$ScStr_00_33 + data$ScStr_00_32)/2 + (data$ScStr_00_34 + data$ScStr_00_33)/2 +
  (data$ScStr_00_35 + data$ScStr_00_34)/2 + (data$ScStr_00_36 + data$ScStr_00_35)/2 + (data$ScStr_00_37 + data$ScStr_00_36)/2 + (data$ScStr_00_38 + data$ScStr_00_37)/2 + (data$ScStr_00_39 + data$ScStr_00_38)/2 +
  (data$ScStr_00_40 + data$ScStr_00_39)/2 + (data$ScStr_00_41 + data$ScStr_00_40)/2 + (data$ScStr_00_42 + data$ScStr_00_41)/2 + (data$ScStr_00_43 + data$ScStr_00_42)/2 + (data$ScStr_00_44 + data$ScStr_00_43)/2 +
  (data$ScStr_00_45 + data$ScStr_00_44)/2 + (data$ScStr_00_46 + data$ScStr_00_45)/2 + (data$ScStr_00_47 + data$ScStr_00_46)/2 + (data$ScStr_00_48 + data$ScStr_00_47)/2 + (data$ScStr_00_49 + data$ScStr_00_48)/2 +
  (data$ScStr_00_50 + data$ScStr_00_49)/2 + (data$ScStr_00_51 + data$ScStr_00_50)/2 + (data$ScStr_00_52 + data$ScStr_00_51)/2 + (data$ScStr_00_53 + data$ScStr_00_52)/2 + (data$ScStr_00_54 + data$ScStr_00_53)/2 +
  (data$ScStr_00_55 + data$ScStr_00_54)/2 + (data$ScStr_00_56 + data$ScStr_00_55)/2 + (data$ScStr_00_57 + data$ScStr_00_56)/2 + (data$ScStr_00_58 + data$ScStr_00_57)/2 + (data$ScStr_00_59 + data$ScStr_00_58)/2 +
  (data$ScStr_01_00 + data$ScStr_00_59)/2 + 
  (data$ScStr_01_01 + data$ScStr_01_00)/2 + (data$ScStr_01_02 + data$ScStr_01_01)/2 + (data$ScStr_01_03 + data$ScStr_01_02)/2 + (data$ScStr_01_04 + data$ScStr_01_03)/2 +
  (data$ScStr_01_05 + data$ScStr_01_04)/2 + (data$ScStr_01_06 + data$ScStr_01_05)/2 + (data$ScStr_01_07 + data$ScStr_01_06)/2 + (data$ScStr_01_08 + data$ScStr_01_07)/2 + (data$ScStr_01_09 + data$ScStr_01_08)/2 + 
  (data$ScStr_01_10 + data$ScStr_01_09)/2 + (data$ScStr_01_11 + data$ScStr_01_10)/2 + (data$ScStr_01_12 + data$ScStr_01_11)/2 + (data$ScStr_01_13 + data$ScStr_01_12)/2 + (data$ScStr_01_14 + data$ScStr_01_13)/2 +
  (data$ScStr_01_15 + data$ScStr_01_14)/2 + (data$ScStr_01_16 + data$ScStr_01_15)/2 + (data$ScStr_01_17 + data$ScStr_01_16)/2 + (data$ScStr_01_18 + data$ScStr_01_17)/2 + (data$ScStr_01_19 + data$ScStr_01_18)/2 +
  (data$ScStr_01_20 + data$ScStr_01_19)/2 + (data$ScStr_01_21 + data$ScStr_01_20)/2 + (data$ScStr_01_22 + data$ScStr_01_21)/2 + (data$ScStr_01_23 + data$ScStr_01_22)/2 + (data$ScStr_01_24 + data$ScStr_01_23)/2 +
  (data$ScStr_01_25 + data$ScStr_01_24)/2 + (data$ScStr_01_26 + data$ScStr_01_25)/2 + (data$ScStr_01_27 + data$ScStr_01_26)/2 + (data$ScStr_01_28 + data$ScStr_01_27)/2 + (data$ScStr_01_29 + data$ScStr_01_28)/2 +
  (data$ScStr_01_30 + data$ScStr_01_29)/2 + (data$ScStr_01_31 + data$ScStr_01_30)/2 + (data$ScStr_01_32 + data$ScStr_01_31)/2 + (data$ScStr_01_33 + data$ScStr_01_32)/2 + (data$ScStr_01_34 + data$ScStr_01_33)/2 +
  (data$ScStr_01_35 + data$ScStr_01_34)/2 + (data$ScStr_01_36 + data$ScStr_01_35)/2 + (data$ScStr_01_37 + data$ScStr_01_36)/2 + (data$ScStr_01_38 + data$ScStr_01_37)/2 + (data$ScStr_01_39 + data$ScStr_01_38)/2 +
  (data$ScStr_01_40 + data$ScStr_01_39)/2 + (data$ScStr_01_41 + data$ScStr_01_40)/2 + (data$ScStr_01_42 + data$ScStr_01_41)/2 + (data$ScStr_01_43 + data$ScStr_01_42)/2 + (data$ScStr_01_44 + data$ScStr_01_43)/2 +
  (data$ScStr_01_45 + data$ScStr_01_44)/2 + (data$ScStr_01_46 + data$ScStr_01_45)/2 + (data$ScStr_01_47 + data$ScStr_01_46)/2 + (data$ScStr_01_48 + data$ScStr_01_47)/2 + (data$ScStr_01_49 + data$ScStr_01_48)/2 +
  (data$ScStr_01_50 + data$ScStr_01_49)/2 + (data$ScStr_01_51 + data$ScStr_01_50)/2 + (data$ScStr_01_52 + data$ScStr_01_51)/2 + (data$ScStr_01_53 + data$ScStr_01_52)/2 + (data$ScStr_01_54 + data$ScStr_01_53)/2 +
  (data$ScStr_01_55 + data$ScStr_01_54)/2 + (data$ScStr_01_56 + data$ScStr_01_55)/2 + (data$ScStr_01_57 + data$ScStr_01_56)/2 + (data$ScStr_01_58 + data$ScStr_01_57)/2 + (data$ScStr_01_59 + data$ScStr_01_58)/2 +
  (data$ScStr_02_00 + data$ScStr_01_59)/2 +
  (data$ScStr_02_01 + data$ScStr_02_00)/2 + (data$ScStr_02_02 + data$ScStr_02_01)/2 + (data$ScStr_02_03 + data$ScStr_02_02)/2 + (data$ScStr_02_04 + data$ScStr_02_03)/2 +
  (data$ScStr_02_05 + data$ScStr_02_04)/2 + (data$ScStr_02_06 + data$ScStr_02_05)/2 + (data$ScStr_02_07 + data$ScStr_02_06)/2 + (data$ScStr_02_08 + data$ScStr_02_07)/2 + (data$ScStr_02_09 + data$ScStr_02_08)/2 + 
  (data$ScStr_02_10 + data$ScStr_02_09)/2 + (data$ScStr_02_11 + data$ScStr_02_10)/2 + (data$ScStr_02_12 + data$ScStr_02_11)/2 + (data$ScStr_02_13 + data$ScStr_02_12)/2 + (data$ScStr_02_14 + data$ScStr_02_13)/2 +
  (data$ScStr_02_15 + data$ScStr_02_14)/2 + (data$ScStr_02_16 + data$ScStr_02_15)/2 + (data$ScStr_02_17 + data$ScStr_02_16)/2 + (data$ScStr_02_18 + data$ScStr_02_17)/2 + (data$ScStr_02_19 + data$ScStr_02_18)/2 +
  (data$ScStr_02_20 + data$ScStr_02_19)/2 + (data$ScStr_02_21 + data$ScStr_02_20)/2 + (data$ScStr_02_22 + data$ScStr_02_21)/2 + (data$ScStr_02_23 + data$ScStr_02_22)/2 + (data$ScStr_02_24 + data$ScStr_02_23)/2 +
  (data$ScStr_02_25 + data$ScStr_02_24)/2 + (data$ScStr_02_26 + data$ScStr_02_25)/2 + (data$ScStr_02_27 + data$ScStr_02_26)/2 + (data$ScStr_02_28 + data$ScStr_02_27)/2 + (data$ScStr_02_29 + data$ScStr_02_28)/2 +
  (data$ScStr_02_30 + data$ScStr_02_29)/2 + (data$ScStr_02_31 + data$ScStr_02_30)/2 + (data$ScStr_02_32 + data$ScStr_02_31)/2 + (data$ScStr_02_33 + data$ScStr_02_32)/2 + (data$ScStr_02_34 + data$ScStr_02_33)/2 +
  (data$ScStr_02_35 + data$ScStr_02_34)/2 + (data$ScStr_02_36 + data$ScStr_02_35)/2 + (data$ScStr_02_37 + data$ScStr_02_36)/2 + (data$ScStr_02_38 + data$ScStr_02_37)/2 + (data$ScStr_02_39 + data$ScStr_02_38)/2 +
  (data$ScStr_02_40 + data$ScStr_02_39)/2 + (data$ScStr_02_41 + data$ScStr_02_40)/2 + (data$ScStr_02_42 + data$ScStr_02_41)/2 + (data$ScStr_02_43 + data$ScStr_02_42)/2 + (data$ScStr_02_44 + data$ScStr_02_43)/2 +
  (data$ScStr_02_45 + data$ScStr_02_44)/2 + (data$ScStr_02_46 + data$ScStr_02_45)/2 + (data$ScStr_02_47 + data$ScStr_02_46)/2 + (data$ScStr_02_48 + data$ScStr_02_47)/2 + (data$ScStr_02_49 + data$ScStr_02_48)/2 +
  (data$ScStr_02_50 + data$ScStr_02_49)/2 + (data$ScStr_02_51 + data$ScStr_02_50)/2 + (data$ScStr_02_52 + data$ScStr_02_51)/2 + (data$ScStr_02_53 + data$ScStr_02_52)/2 + (data$ScStr_02_54 + data$ScStr_02_53)/2 +
  (data$ScStr_02_55 + data$ScStr_02_54)/2 + (data$ScStr_02_56 + data$ScStr_02_55)/2 + (data$ScStr_02_57 + data$ScStr_02_56)/2 + (data$ScStr_02_58 + data$ScStr_02_57)/2 + (data$ScStr_02_59 + data$ScStr_02_58)/2 +
  (data$ScStr_03_00 + data$ScStr_02_59)/2 +
  (data$ScStr_03_01 + data$ScStr_03_00)/2 + (data$ScStr_03_02 + data$ScStr_03_01)/2 + (data$ScStr_03_03 + data$ScStr_03_02)/2 + (data$ScStr_03_04 + data$ScStr_03_03)/2 +
  (data$ScStr_03_05 + data$ScStr_03_04)/2 + (data$ScStr_03_06 + data$ScStr_03_05)/2 + (data$ScStr_03_07 + data$ScStr_03_06)/2 + (data$ScStr_03_08 + data$ScStr_03_07)/2 + (data$ScStr_03_09 + data$ScStr_03_08)/2 + 
  (data$ScStr_03_10 + data$ScStr_03_09)/2 + (data$ScStr_03_11 + data$ScStr_03_10)/2 + (data$ScStr_03_12 + data$ScStr_03_11)/2 + (data$ScStr_03_13 + data$ScStr_03_12)/2 + (data$ScStr_03_14 + data$ScStr_03_13)/2 +
  (data$ScStr_03_15 + data$ScStr_03_14)/2 + (data$ScStr_03_16 + data$ScStr_03_15)/2 + (data$ScStr_03_17 + data$ScStr_03_16)/2 + (data$ScStr_03_18 + data$ScStr_03_17)/2 + (data$ScStr_03_19 + data$ScStr_03_18)/2 +
  (data$ScStr_03_20 + data$ScStr_03_19)/2 + (data$ScStr_03_21 + data$ScStr_03_20)/2 + (data$ScStr_03_22 + data$ScStr_03_21)/2 + (data$ScStr_03_23 + data$ScStr_03_22)/2 + (data$ScStr_03_24 + data$ScStr_03_23)/2 +
  (data$ScStr_03_25 + data$ScStr_03_24)/2 + (data$ScStr_03_26 + data$ScStr_03_25)/2 + (data$ScStr_03_27 + data$ScStr_03_26)/2 + (data$ScStr_03_28 + data$ScStr_03_27)/2 + (data$ScStr_03_29 + data$ScStr_03_28)/2 +
  (data$ScStr_03_30 + data$ScStr_03_29)/2 + (data$ScStr_03_31 + data$ScStr_03_30)/2 + (data$ScStr_03_32 + data$ScStr_03_31)/2 + (data$ScStr_03_33 + data$ScStr_03_32)/2 + (data$ScStr_03_34 + data$ScStr_03_33)/2 +
  (data$ScStr_03_35 + data$ScStr_03_34)/2 + (data$ScStr_03_36 + data$ScStr_03_35)/2 + (data$ScStr_03_37 + data$ScStr_03_36)/2 + (data$ScStr_03_38 + data$ScStr_03_37)/2 + (data$ScStr_03_39 + data$ScStr_03_38)/2 +
  (data$ScStr_03_40 + data$ScStr_03_39)/2 + (data$ScStr_03_41 + data$ScStr_03_40)/2 + (data$ScStr_03_42 + data$ScStr_03_41)/2 + (data$ScStr_03_43 + data$ScStr_03_42)/2 + (data$ScStr_03_44 + data$ScStr_03_43)/2 +
  (data$ScStr_03_45 + data$ScStr_03_44)/2 + (data$ScStr_03_46 + data$ScStr_03_45)/2 + (data$ScStr_03_47 + data$ScStr_03_46)/2 + (data$ScStr_03_48 + data$ScStr_03_47)/2 + (data$ScStr_03_49 + data$ScStr_03_48)/2 +
  (data$ScStr_03_50 + data$ScStr_03_49)/2 + (data$ScStr_03_51 + data$ScStr_03_50)/2 + (data$ScStr_03_52 + data$ScStr_03_51)/2 + (data$ScStr_03_53 + data$ScStr_03_52)/2 + (data$ScStr_03_54 + data$ScStr_03_53)/2 +
  (data$ScStr_03_55 + data$ScStr_03_54)/2 + (data$ScStr_03_56 + data$ScStr_03_55)/2 + (data$ScStr_03_57 + data$ScStr_03_56)/2 + (data$ScStr_03_58 + data$ScStr_03_57)/2 + (data$ScStr_03_59 + data$ScStr_03_58)/2 

data$SSSCCombAUCi <- data$SSSCCombAUCg - (239 * data$ScStr_00_00)

AUC Countdown

HRR Signaled

data$CDHRCombAucgSigaled <- (data$HrStr_00_13 + data$HrStr_00_12)/2 + (data$HrStr_00_14 + data$HrStr_00_13)/2 + (data$HrStr_00_15 + data$HrStr_00_14)/2 + (data$HrStr_00_16 + data$HrStr_00_15)/2 + (data$HrStr_00_17 + data$HrStr_00_16)/2 + (data$HrStr_00_18 + data$HrStr_00_17)/2 + (data$HrStr_00_19 + data$HrStr_00_18)/2 + (data$HrStr_00_20 + data$HrStr_00_19)/2 + (data$HrStr_00_21 + data$HrStr_00_20)/2 + (data$HrStr_00_22 + data$HrStr_00_21)/2 + (data$HrStr_00_23 + data$HrStr_00_22) + (data$HrStr_00_26 + data$HrStr_00_25)/2 + (data$HrStr_00_27 + data$HrStr_00_26)/2 + (data$HrStr_00_28 + data$HrStr_00_27)/2 + (data$HrStr_00_29 + data$HrStr_00_28)/2 + (data$HrStr_00_30 + data$HrStr_00_29)/2 + (data$HrStr_00_31 + data$HrStr_00_30)/2 + (data$HrStr_00_32 + data$HrStr_00_31)/2 + (data$HrStr_00_33 + data$HrStr_00_32)/2 + (data$HrStr_00_34 + data$HrStr_00_33)/2 + (data$HrStr_00_35 + data$HrStr_00_34)/2 + (data$HrStr_00_36 + data$HrStr_00_35)/2 + (data$HrStr_00_37 + data$HrStr_00_36)/2 + (data$HrStr_00_38 + data$HrStr_00_37)/2 + (data$HrStr_00_39 + data$HrStr_00_38)/2 + (data$HrStr_00_40 + data$HrStr_00_39)/2 + (data$HrStr_00_41 + data$HrStr_00_40)/2 + (data$HrStr_00_42 + data$HrStr_00_41)/2 + (data$HrStr_00_43 + data$HrStr_00_42)/2 + (data$HrStr_00_44 + data$HrStr_00_43)/2 + (data$HrStr_01_43 + data$HrStr_01_42)/2 + (data$HrStr_01_44 + data$HrStr_01_43)/2 + (data$HrStr_01_45 + data$HrStr_01_44)/2 + (data$HrStr_01_46 + data$HrStr_01_45)/2 + (data$HrStr_01_47 + data$HrStr_01_46)/2 + (data$HrStr_01_48 + data$HrStr_01_47)/2 + (data$HrStr_01_49 + data$HrStr_01_48)/2 + (data$HrStr_01_50 + data$HrStr_01_49)/2 + (data$HrStr_01_51 + data$HrStr_01_50)/2 + (data$HrStr_01_52 + data$HrStr_01_51)/2 + (data$HrStr_01_53 + data$HrStr_01_52)/2 + (data$HrStr_01_56 + data$HrStr_01_55)/2 + (data$HrStr_01_57 + data$HrStr_01_56)/2 + (data$HrStr_01_58 + data$HrStr_01_57)/2 + (data$HrStr_01_59 + data$HrStr_01_58)/2 + (data$HrStr_02_00 + data$HrStr_01_59)/2 + (data$HrStr_02_01 + data$HrStr_02_00)/2 + (data$HrStr_02_02 + data$HrStr_02_01)/2 + (data$HrStr_02_03 + data$HrStr_02_02)/2 + (data$HrStr_02_04 + data$HrStr_02_03)/2 + (data$HrStr_02_05 + data$HrStr_02_04)/2 + (data$HrStr_02_06 + data$HrStr_02_05)/2 + (data$HrStr_02_07 + data$HrStr_02_06)/2 + (data$HrStr_02_08 + data$HrStr_02_07)/2 + (data$HrStr_02_09 + data$HrStr_02_08)/2 + (data$HrStr_02_10 + data$HrStr_02_09)/2 + (data$HrStr_02_11 + data$HrStr_02_12)/2 + (data$HrStr_02_12 + data$HrStr_02_11)/2 + (data$HrStr_02_13 + data$HrStr_02_12)/2 + (data$HrStr_02_14 + data$HrStr_02_13)/2

data$CDHRCombAuciSigaled <- data$CDHRCombAucgSigaled  - (63 * data$HrStr_00_12)

HRR Unsignaled

data$CDHRCombAucgUnSigaled <- (data$HrStr_00_58 + data$HrStr_00_57)/2 + (data$HrStr_00_59 + data$HrStr_00_58)/2 + (data$HrStr_01_00 + data$HrStr_00_59)/2 + (data$HrStr_01_01 + data$HrStr_01_00)/2 + (data$HrStr_01_02 + data$HrStr_01_01)/2 + (data$HrStr_01_03 + data$HrStr_01_02)/2 + (data$HrStr_01_04 + data$HrStr_01_03)/2 + (data$HrStr_01_05 + data$HrStr_01_04)/2 + (data$HrStr_01_06 + data$HrStr_01_05)/2 + (data$HrStr_01_07 + data$HrStr_01_06)/2 + (data$HrStr_01_08 + data$HrStr_01_07)/2 + (data$HrStr_01_11 + data$HrStr_01_10)/2 + (data$HrStr_01_12 + data$HrStr_01_11)/2 + (data$HrStr_01_13 + data$HrStr_01_12)/2 + (data$HrStr_01_14 + data$HrStr_01_13)/2 + (data$HrStr_01_15 + data$HrStr_01_14)/2 + (data$HrStr_01_16 + data$HrStr_01_15)/2 + (data$HrStr_01_17 + data$HrStr_01_16)/2 + (data$HrStr_01_18 + data$HrStr_01_17)/2 + (data$HrStr_01_19 + data$HrStr_01_18)/2 + (data$HrStr_01_20 + data$HrStr_01_19)/2 + (data$HrStr_01_21 + data$HrStr_01_20)/2 + (data$HrStr_01_22 + data$HrStr_01_21)/2 + (data$HrStr_01_23 + data$HrStr_01_22)/2 + (data$HrStr_01_24 + data$HrStr_01_23)/2 + (data$HrStr_01_25 + data$HrStr_01_24)/2 + (data$HrStr_01_26 + data$HrStr_01_25)/2 + (data$HrStr_01_27 + data$HrStr_01_26)/2 + (data$HrStr_01_28 + data$HrStr_01_27)/2 + (data$HrStr_01_29 + data$HrStr_01_28)/2 + (data$HrStr_02_28 + data$HrStr_02_27)/2 + (data$HrStr_02_29 + data$HrStr_02_28)/2 + (data$HrStr_02_29 + data$HrStr_02_28)/2 + (data$HrStr_02_30 + data$HrStr_02_29)/2 + (data$HrStr_02_31 + data$HrStr_02_30)/2 + (data$HrStr_02_32 + data$HrStr_02_31)/2 + (data$HrStr_02_33 + data$HrStr_02_32)/2 + (data$HrStr_02_34 + data$HrStr_02_33)/2 + (data$HrStr_02_35 + data$HrStr_02_34)/2 + (data$HrStr_02_36 + data$HrStr_02_35)/2 + (data$HrStr_02_37 + data$HrStr_02_36)/2 + (data$HrStr_02_38 + data$HrStr_02_37)/2 + (data$HrStr_02_41 + data$HrStr_02_40)/2 + (data$HrStr_02_42 + data$HrStr_02_41)/2 + (data$HrStr_02_43 + data$HrStr_02_42)/2 + (data$HrStr_02_44 + data$HrStr_02_43)/2 + (data$HrStr_02_45 + data$HrStr_02_44)/2 + (data$HrStr_02_46 + data$HrStr_02_45)/2 + (data$HrStr_02_47 + data$HrStr_02_46)/2 + (data$HrStr_02_48 + data$HrStr_02_47)/2 + (data$HrStr_02_49 + data$HrStr_02_48)/2 + (data$HrStr_02_50 + data$HrStr_02_49)/2 + (data$HrStr_02_51 + data$HrStr_02_50)/2 + (data$HrStr_02_52 + data$HrStr_02_51)/2 + (data$HrStr_02_53 + data$HrStr_02_52)/2 + (data$HrStr_02_54 + data$HrStr_02_53)/2 + (data$HrStr_02_55 + data$HrStr_02_54)/2 + (data$HrStr_02_56 + data$HrStr_02_55)/2 + (data$HrStr_02_57 + data$HrStr_02_56)/2 + (data$HrStr_02_58 + data$HrStr_02_57)/2 + (data$HrStr_02_59 + data$HrStr_02_58)/2 


data$CDHRCombAuciUnSigaled <- data$CDHRCombAucgUnSigaled - (63 * data$HrStr_00_57)

SC Signaled

data$CDSCCombAucgSigaled <- (data$ScStr_00_13 + data$ScStr_00_12)/2 + (data$ScStr_00_14 + data$ScStr_00_13)/2 + (data$ScStr_00_15 + data$ScStr_00_14)/2 + (data$ScStr_00_16 + data$ScStr_00_15)/2 + (data$ScStr_00_17 + data$ScStr_00_16)/2 + (data$ScStr_00_18 + data$ScStr_00_17)/2 + (data$ScStr_00_19 + data$ScStr_00_18)/2 + (data$ScStr_00_20 + data$ScStr_00_19)/2 + (data$ScStr_00_21 + data$ScStr_00_20)/2 + (data$ScStr_00_22 + data$ScStr_00_21)/2 + (data$ScStr_00_23 + data$ScStr_00_22)/2 + (data$ScStr_00_26 + data$ScStr_00_25)/2 + (data$ScStr_00_27 + data$ScStr_00_26)/2 + (data$ScStr_00_28 + data$ScStr_00_27)/2 + (data$ScStr_00_29 + data$ScStr_00_28)/2 + (data$ScStr_00_30 + data$ScStr_00_29)/2 + (data$ScStr_00_31 + data$ScStr_00_30)/2 + (data$ScStr_00_32 + data$ScStr_00_31)/2 + (data$ScStr_00_33 + data$ScStr_00_32)/2 + (data$ScStr_00_34 + data$ScStr_00_33)/2 + (data$ScStr_00_35 + data$ScStr_00_34)/2 + (data$ScStr_00_36 + data$ScStr_00_35)/2 + (data$ScStr_00_37 + data$ScStr_00_36)/2 + (data$ScStr_00_38 + data$ScStr_00_37)/2 + (data$ScStr_00_39 + data$ScStr_00_38)/2 + (data$ScStr_00_40 + data$ScStr_00_39)/2 + (data$ScStr_00_41 + data$ScStr_00_40)/2 + (data$ScStr_00_42 + data$ScStr_00_41)/2 + (data$ScStr_00_43 + data$ScStr_00_42)/2 + (data$ScStr_00_44 + data$ScStr_00_43)/2 + (data$ScStr_01_43 + data$ScStr_01_42)/2 + (data$ScStr_01_44 + data$ScStr_01_43)/2 + (data$ScStr_01_45 + data$ScStr_01_44)/2 + (data$ScStr_01_46 + data$ScStr_01_45)/2 + (data$ScStr_01_47 + data$ScStr_01_46)/2 + (data$ScStr_01_48 + data$ScStr_01_47)/2 + (data$ScStr_01_49 + data$ScStr_01_48)/2 + (data$ScStr_01_50 + data$ScStr_01_49)/2 + (data$ScStr_01_51 + data$ScStr_01_50)/2 + (data$ScStr_01_52 + data$ScStr_01_51)/2 + (data$ScStr_01_53 + data$ScStr_01_52)/2 + (data$ScStr_01_56 + data$ScStr_01_55)/2 + (data$ScStr_01_57 + data$ScStr_01_56)/2 + (data$ScStr_01_58 + data$ScStr_01_57)/2 + (data$ScStr_01_59 + data$ScStr_01_58)/2 + (data$ScStr_02_00 + data$ScStr_01_59)/2 + (data$ScStr_02_01 + data$ScStr_02_00)/2 + (data$ScStr_02_02 + data$ScStr_02_01)/2 + (data$ScStr_02_03 + data$ScStr_02_02)/2 + (data$ScStr_02_04 + data$ScStr_02_03)/2 + (data$ScStr_02_05 + data$ScStr_02_04)/2 + (data$ScStr_02_06 + data$ScStr_02_05)/2 + (data$ScStr_02_07 + data$ScStr_02_06)/2 + (data$ScStr_02_08 + data$ScStr_02_07)/2 + (data$ScStr_02_09 + data$ScStr_02_08)/2 + (data$ScStr_02_10 + data$ScStr_02_09)/2 + (data$ScStr_02_11 + data$ScStr_02_10)/2 + (data$ScStr_02_12 + data$ScStr_02_11)/2 + (data$ScStr_02_13 + data$ScStr_02_12)/2 + (data$ScStr_02_14 + data$ScStr_02_13)/2 

data$CDSCCombAuciSigaled <- data$CDSCCombAucgSigaled - (63 * data$ScStr_00_12)

SC Unsignaled

data$CDSCCombAucgUnSigaled <- (data$ScStr_00_58 + data$ScStr_00_57)/2 + (data$ScStr_00_59 + data$ScStr_00_58)/2 + (data$ScStr_01_00 + data$ScStr_00_59)/2 + (data$ScStr_01_01 + data$ScStr_01_00)/2 + (data$ScStr_01_02 + data$ScStr_01_01)/2 + (data$ScStr_01_03 + data$ScStr_01_02)/2 + (data$ScStr_01_04 + data$ScStr_01_03)/2 + (data$ScStr_01_05 + data$ScStr_01_04)/2 + (data$ScStr_01_06 + data$ScStr_01_05)/2 + (data$ScStr_01_07 + data$ScStr_01_06)/2 + (data$ScStr_01_08 + data$ScStr_01_07)/2 + (data$ScStr_01_11 + data$ScStr_01_10)/2 + (data$ScStr_01_12 + data$ScStr_01_11)/2 + (data$ScStr_01_13 + data$ScStr_01_12)/2 + (data$ScStr_01_14 + data$ScStr_01_13)/2 + (data$ScStr_01_15 + data$ScStr_01_14)/2 + (data$ScStr_01_16 + data$ScStr_01_15)/2 + (data$ScStr_01_17 + data$ScStr_01_16)/2 + (data$ScStr_01_18 + data$ScStr_01_17)/2 + (data$ScStr_01_19 + data$ScStr_01_18)/2 + (data$ScStr_01_20 + data$ScStr_01_19)/2 + (data$ScStr_01_21 + data$ScStr_01_20)/2 + (data$ScStr_01_22 + data$ScStr_01_21)/2 + (data$ScStr_01_23 + data$ScStr_01_22)/2 + (data$ScStr_01_24 + data$ScStr_01_23)/2 + (data$ScStr_01_25 + data$ScStr_01_24)/2 + (data$ScStr_01_26 + data$ScStr_01_25)/2 + (data$ScStr_01_27 + data$ScStr_01_26)/2 + (data$ScStr_01_28 + data$ScStr_01_27)/2 + (data$ScStr_01_29 + data$ScStr_01_28)/2 + (data$ScStr_02_28 + data$ScStr_02_27)/2 + (data$ScStr_02_29 + data$ScStr_02_28)/2 + (data$ScStr_02_30 + data$ScStr_02_29)/2 + (data$ScStr_02_31 + data$ScStr_02_30)/2 + (data$ScStr_02_32 + data$ScStr_02_31)/2 + (data$ScStr_02_33 + data$ScStr_02_32)/2 + (data$ScStr_02_34 + data$ScStr_02_33)/2 + (data$ScStr_02_35 + data$ScStr_02_34)/2 + (data$ScStr_02_36 + data$ScStr_02_35)/2 + (data$ScStr_02_37 + data$ScStr_02_36)/2 + (data$ScStr_02_38 + data$ScStr_02_37) + (data$ScStr_02_41 + data$ScStr_02_40)/2 + (data$ScStr_02_42 + data$ScStr_02_41)/2 + (data$ScStr_02_43 + data$ScStr_02_42)/2 + (data$ScStr_02_44 + data$ScStr_02_43)/2 + (data$ScStr_02_45 + data$ScStr_02_44)/2 + (data$ScStr_02_46 + data$ScStr_02_45)/2 + (data$ScStr_02_47 + data$ScStr_02_46)/2 + (data$ScStr_02_48 + data$ScStr_02_47)/2 + (data$ScStr_02_49 + data$ScStr_02_48)/2 + (data$ScStr_02_50 + data$ScStr_02_49)/2 + (data$ScStr_02_51 + data$ScStr_02_50)/2 + (data$ScStr_02_52 + data$ScStr_02_51)/2 + (data$ScStr_02_53 + data$ScStr_02_52)/2 + (data$ScStr_02_54 + data$ScStr_02_53)/2 + (data$ScStr_02_55 + data$ScStr_02_54)/2 + (data$ScStr_02_56 + data$ScStr_02_55)/2 + (data$ScStr_02_57 + data$ScStr_02_56)/2 + (data$ScStr_02_58 + data$ScStr_02_57)/2 + (data$ScStr_02_59 + data$ScStr_02_58)/2

data$CDSCCombAuciUnSigaled <- data$CDSCCombAucgUnSigaled - (63 * data$ScStr_00_57)

Wrangling

Full Sample

Table 1

#Full

FullsampleFinalSurveyT1 <- data |> 
   dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal)

FSFSurveyT1 <- FullsampleFinalSurveyT1 |> 
  na.omit()

FullsampleFinalHRT1 <- data |> 
   dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, HRbaseline)

FSFHRT1 <- FullsampleFinalHRT1 |> 
  na.omit()


FullsampleFinalSCT1 <- data |> 
  dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SCbaseline)

FSFSCT1 <- FullsampleFinalSCT1 |> 
  na.omit()

# Social Stressor 

SocialStressorFinalHRT1 <- data |> 
   dplyr::select(Task, White, Gender, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSHRCombAUCi, HRbaseline) |> 
   filter(Task == "2")

SSFHRT1 <- SocialStressorFinalHRT1 |> 
  na.omit()


SocialStressorFinalSCT1 <- data |> 
  dplyr::select(Task, White, Gender, Male, Female, Age, GenderNumb,SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSSCCombAUCi, SCbaseline) |> 
  filter(Task == "2")

SSFSCT1 <- SocialStressorFinalSCT1 |> 
  na.omit()


# Countdown 

CountdownFinalHRT1 <- data |> 
   dplyr::select(Task, Male, Female, White, Gender, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, CDHRCombAuciSigaled, CDHRCombAuciUnSigaled, HRbaseline) |> 
   filter(Task == "1")

CDFHRT1 <- CountdownFinalHRT1 |> 
   na.omit()
  
CountdownFinalSCT1 <- data |> 
   dplyr::select(Task, Male, Female, White, Gender, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal, SSSThrilTotal, SSSExpTotal,
                 CDSCCombAuciSigaled, CDSCCombAuciUnSigaled, SCbaseline) |> 
   filter(Task == "1")
 
CDFSCT1 <- CountdownFinalSCT1 |> 
   na.omit()

Male Only

Table 1

These data frames were required to compensate for the missing variables. If I just selected the one column I needed (e.g.,“HRbaseline”) the missing would not match the true sample number because missing values are contained within the survey. This is most evident in the female sample (Female Only Table 1 code chunk).

# Baseline

MaleHRbaseT1 <- data |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, HRbaseline) |> 
  filter(GenderNumb == "2")

MHRbT1 <- MaleHRbaseT1 |> 
  na.omit()


MaleSCbaseT1 <- data |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SCbaseline) |> 
  filter(GenderNumb == "2")

MSCbT1 <- MaleSCbaseT1 |> 
  na.omit()


# Social Stressor 

MaleSSHRT1 <- SocialStressorFinalHRT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSHRCombAUCi) |>  
  filter(GenderNumb == "2")

MSSHRT1 <- MaleSSHRT1 |> 
  na.omit()

MaleSSSCT1 <- SocialStressorFinalSCT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSSCCombAUCi) |> 
  filter(GenderNumb == "2")

MSSSCT1 <- MaleSSSCT1 |> 
  na.omit()

# Countdown 


MaleCDHRT1 <- CountdownFinalHRT1 |> 
  dplyr::select(GenderNumb,  White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, CDHRCombAuciSigaled, CDHRCombAuciUnSigaled) |> 
  filter(GenderNumb == "2")

MCDHRT1 <- MaleCDHRT1 |> 
  na.omit()

MaleCDSCT1 <- CountdownFinalSCT1 |> 
  dplyr::select(GenderNumb,  White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, CDSCCombAuciSigaled, CDSCCombAuciUnSigaled) |>  
  filter(GenderNumb == "2")

MCDSCT1 <- MaleCDSCT1 |> 
  na.omit()

Female Only

Distribution Checks

# Survey only for distribution checks 

FemaleDistribCheck <-  data |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal) |> 
  filter(GenderNumb == "1")


FemaleDisCheck <- FemaleDistribCheck |> 
  na.omit()

Table 1

# baseline 

FemaleHRbaselineT1 <-  data |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, HRbaseline) |> 
  filter(GenderNumb == "1")


FemaleHRbaseT1 <- FemaleHRbaselineT1 |> 
  na.omit()

FemaleSCbaselineT1 <- data |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SCbaseline) |> 
  filter(GenderNumb == "1")

FemaleSCbaseT1 <- FemaleSCbaselineT1 |> 
  na.omit()

# Social Stressor 

FemaleSocialSHRT1 <- SocialStressorFinalHRT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSHRCombAUCi) |> 
  filter(GenderNumb == "1")

FemaleSSHRT1 <- FemaleSocialSHRT1 |> 
  na.omit()


FemaleSocialSSCT1 <- SocialStressorFinalSCT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, SSSCCombAUCi) |> 
  filter(GenderNumb == "1")

FemaleSCSST1 <- FemaleSocialSSCT1 |> 
  na.omit()

# Countdown 

FemaleHRCountDT1 <- CountdownFinalHRT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, CDHRCombAuciSigaled, CDHRCombAuciUnSigaled) |> 
  filter(GenderNumb == "1")

FemaleHRCDT1 <- FemaleHRCountDT1 |> 
  na.omit()



FemaleSCCountDT1 <- CountdownFinalSCT1 |> 
  dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal, CDSCCombAuciSigaled, CDSCCombAuciUnSigaled) |>  
  filter(GenderNumb == "1")

FemaleSCCDT1 <- FemaleSCCountDT1 |> 
  na.omit()

Scale Reliability Data Frame

Created a new data frame that takes into account the sample and the missing data to calculate the alphas. Can’t use original data frome due to the nature of the autonomic data (i.e., contains a varying amount NAs for all participants, therefore can’t na.omit). To save time and reduce error, I used gsub(). Process below to replicate.

Code <- “Copy and paste the code from the total scores”

new_code <- gsub(“data\$”, ““, code) ^ use an escape character (i.e., \) to treat the $ as normal character and not a special expression. Throws in a”” spot.

new_code2 <- gsub(“\+”, “,”, new_code) ^ same story here.

cat(new_code2) ^ Concatenate the variables into a neat string and copy from console to code chunk.

Scaledf <- data |> 
  dplyr::select (SRP_01n , SRP_02n , SRP_03n , SRP_04n ,  SRP5nRev , SRP6nRev , SRP_07n , SRP_08n ,
                 SRP_09n , SRP_10n , SRP11nRev , SRP_12n , SRP_13n , SRP14nRev ,
                  SRP_15n , SRP16nRev , SRP_17n , SRP18nRev , SRP19nRev , SRP_20n , SRP21nRev ,
                  SRP22nRev , SRP23nRev , SRP24nRev , SRP25nRev , SRP26nRev , SRP_27n , SRP_28n , 
                  SRP_29n , SRP_30n , SRP31nRev , SRP_32n , SRP_33n , SRP34nRev ,  SRP_35n , 
                  SRP36nRev ,  SRP_37n , SRP38nRev , SRP_39n , SRP_40n , SRP_41n , SRP_42n , 
                  SRP_43n , SRP44nRev , SRP_45n , SRP46nRev , SRP47nRev , SRP_48n , SRP_49n , 
                  SRP_50n , SRP_51n , SRP_52n , SRP_53n , SRP_54n , SRP_55n ,  SRP_56n ,
                  SRP_57n , SRP_58n , SRP_59n , SRP_60n , SRP61nRev , SRP_62n , SRP_63n , SRP_64n ,
                  ICU_1nRev , ICU_2n , ICU_3nRev , ICU_4n , ICU_5nRev , ICU_6n , 
                  ICU_7n , ICU_8nRev , ICU_9n , ICU_10n , ICU_11n , ICU_12n , ICU_13nRev ,
                  ICU_14nRev , ICU_15nRev , ICU_16nRev , ICU_17nRev , ICU_18n , ICU_19nRev ,
                  ICU_20n , ICU_21n , ICU_22n , ICU_23nRev , ICU_24nRev ,
                  Lev_01n , Lev_02n , Lev_03nRev , Lev_04n , Lev_05n , Lev_06n , Lev_07nRev , Lev_08n ,
                 Lev_09n , Lev_10nRev , Lev_11n , Lev_12n , Lev_13nRev , Lev_16n , Lev_17n , Lev_18n ,
                 Lev_19n , Lev_20n , Lev_21nRev , Lev_22n , Lev_23n , Lev_24n , Lev_25n , Lev_26nRev , 
                 ZSSS_1nRev , ZSSS_2n , ZSSS_3nRev , ZSSS_4n , ZSSS_5nRev , ZSSS_6nRev , ZSSS_7n , ZSSS_8nRev ,
                 ZSSS_9nRev , ZSSS_10n , ZSSS_11n , ZSSS_12n , ZSSS_13n , ZSSS_14nRev , ZSSS_15n , ZSSS_16nRev , 
                 ZSSS_17nRev , ZSSS_18nRev , ZSSS_19n , ZSSS_20n , ZSSS_21n , ZSSS_22nRev , ZSSS_23nRev , ZSSS_24nRev , 
                 ZSSS_25n , ZSSS_26n , ZSSS_27n , ZSSS_28nRev , ZSSS_29nRev , ZSSS_30n , ZSSS_31n ,
                  ZSSS_32nRev , ZSSS_33n , ZSSS_34nRev , ZSSS_35n , ZSSS_36nRev , ZSSS_37n , ZSSS_38n , ZSSS_39nRev , ZSSS_40n) |> 
  na.omit()
                  
                  

# SRP 

SRPTotA <-Scaledf[ , c("SRP_03n","SRP_08n","SRP_13n","SRP16nRev","SRP_20n","SRP24nRev","SRP_27n",
                    "SRP31nRev","SRP_35n", "SRP38nRev","SRP_41n", "SRP_45n","SRP_50n", "SRP_54n","SRP_58n", "SRP61nRev",
                    "SRP_02n","SRP_07n", "SRP11nRev", "SRP_15n", "SRP19nRev", "SRP23nRev", "SRP26nRev", "SRP_30n", "SRP_33n",
                    "SRP_37n", "SRP_40n", "SRP44nRev", "SRP_48n", "SRP_01n", "SRP_53n", "SRP_56n", "SRP_60n", "SRP_04n", 
                    "SRP_09n", "SRP14nRev", "SRP_17n", "SRP22nRev", "SRP25nRev", "SRP_28n", "SRP_32n", "SRP36nRev", "SRP_39n",
                    "SRP_42n", "SRP47nRev", "SRP_51n", "SRP_55n", "SRP_59n", "SRP5nRev", "SRP6nRev", "SRP_10n", "SRP_12n", 
                    "SRP18nRev", "SRP21nRev", "SRP_29n", "SRP34nRev", "SRP_43n", "SRP46nRev", "SRP_49n", "SRP_52n",
                    "SRP_57n", "SRP_62n", "SRP_63n", "SRP_64n")]

SRPIPMA <-Scaledf[ , c("SRP_03n","SRP_08n","SRP_13n","SRP16nRev","SRP_20n","SRP24nRev","SRP_27n",
                    "SRP31nRev","SRP_35n", "SRP38nRev","SRP_41n", "SRP_45n","SRP_50n", "SRP_54n","SRP_58n", "SRP61nRev")]

SRPICAA <-Scaledf[ , c("SRP_02n","SRP_07n","SRP11nRev","SRP_15n","SRP19nRev","SRP23nRev","SRP26nRev",
                    "SRP_30n","SRP_33n", "SRP_37n","SRP_40n", "SRP44nRev","SRP_48n", "SRP_53n","SRP_56n", "SRP_60n")]

SRPELSA <-Scaledf[ , c("SRP_01n","SRP_04n","SRP_09n","SRP14nRev","SRP_17n","SRP22nRev","SRP25nRev",
                    "SRP_28n","SRP_32n", "SRP36nRev","SRP_39n", "SRP_42n","SRP47nRev", "SRP_51n","SRP_55n", "SRP_59n")]

SRPASBA <-Scaledf[ , c("SRP5nRev","SRP6nRev","SRP_10n","SRP_12n","SRP18nRev","SRP21nRev","SRP_29n",
                    "SRP34nRev","SRP_43n", "SRP46nRev","SRP_49n", "SRP_52n","SRP_57n", "SRP_62n","SRP_63n", "SRP_64n")]

#  ICU 

ICUTotA <-Scaledf[ , c("ICU_1nRev","ICU_2n","ICU_3nRev","ICU_4n","ICU_5nRev","ICU_6n","ICU_7n",
                    "ICU_8nRev","ICU_9n", "ICU_10n", "ICU_11n","ICU_12n", "ICU_13nRev","ICU_14nRev", "ICU_15nRev",
                    "ICU_16nRev","ICU_17nRev", "ICU_18n","ICU_19nRev", "ICU_20n","ICU_21n", "ICU_22n","ICU_23nRev", "ICU_24nRev")]

ICUCalA <-Scaledf[ , c("ICU_4n","ICU_8nRev","ICU_9n","ICU_18n","ICU_11n","ICU_21n","ICU_7n",
                    "ICU_20n","ICU_2n", "ICU_12n","ICU_10n")]

ICUUncareA <-Scaledf[ , c("ICU_15nRev","ICU_23nRev","ICU_16nRev","ICU_3nRev","ICU_17nRev","ICU_24nRev","ICU_13nRev",
                       "ICU_5nRev")]

ICUUnemoA <-Scaledf[ , c("ICU_1nRev","ICU_19nRev","ICU_6n","ICU_22n","ICU_14nRev")]

# LSRP

LevTotA <-Scaledf[ , c("Lev_01n","Lev_02n","Lev_03nRev","Lev_04n","Lev_05n","Lev_06n","Lev_07nRev",
                    "Lev_08n","Lev_09n", "Lev_10nRev","Lev_11n","Lev_12n", "Lev_13nRev","Lev_16n","Lev_17n", "Lev_18n",
                    "Lev_19n", "Lev_20n","Lev_21nRev", "Lev_22n","Lev_23n", "Lev_24n", "Lev_25n","Lev_26nRev" )]

LevPrimA <-Scaledf[ , c("Lev_02n","Lev_04n","Lev_07nRev","Lev_09n","Lev_11n","Lev_12n",
                     "Lev_13nRev","Lev_17n", "Lev_19n","Lev_21nRev","Lev_22n", "Lev_23n","Lev_24n","Lev_25n", "Lev_26nRev")]

LevSecA <-Scaledf[ , c("Lev_01n","Lev_03nRev","Lev_05n","Lev_06n","Lev_08n","Lev_10nRev",
                    "Lev_16n","Lev_18n", "Lev_20n")]
# SSS 

SSSTotA <-Scaledf[ , c("ZSSS_1nRev","ZSSS_2n","ZSSS_3nRev","ZSSS_4n","ZSSS_5nRev","ZSSS_6nRev","ZSSS_7n", "ZSSS_8nRev","ZSSS_9nRev", "ZSSS_10n",
                    "ZSSS_11n", "ZSSS_12n","ZSSS_13n", "ZSSS_14nRev","ZSSS_15n", "ZSSS_16nRev", "ZSSS_17nRev","ZSSS_18nRev", "ZSSS_19n", "ZSSS_20n",
                    "ZSSS_21n", "ZSSS_22nRev", "ZSSS_23nRev", "ZSSS_24nRev", "ZSSS_25n", "ZSSS_26n", "ZSSS_27n","ZSSS_28nRev","ZSSS_29nRev","ZSSS_30n","ZSSS_31n",
                    "ZSSS_32nRev","ZSSS_33n","ZSSS_34nRev","ZSSS_35n","ZSSS_36nRev","ZSSS_37n","ZSSS_38n", "ZSSS_39nRev", "ZSSS_40n")]

SSSDISA <-Scaledf[ , c("ZSSS_12n","ZSSS_13n","ZSSS_25n","ZSSS_30n","ZSSS_33n","ZSSS_35n",
                    "ZSSS_1nRev","ZSSS_29nRev", "ZSSS_32nRev", "ZSSS_36nRev")]

SSSBorA <-Scaledf[ , c("ZSSS_2n","ZSSS_7n","ZSSS_15n","ZSSS_27n","ZSSS_31n","ZSSS_5nRev",
                    "ZSSS_8nRev","ZSSS_24nRev", "ZSSS_34nRev", "ZSSS_39nRev")]

SSSThrilA <-Scaledf[ , c("ZSSS_11n","ZSSS_20n","ZSSS_21n","ZSSS_38n","ZSSS_40n","ZSSS_3nRev",
                      "ZSSS_16nRev","ZSSS_17nRev", "ZSSS_23nRev", "ZSSS_28nRev")]

SSSExpA <-Scaledf[ , c("ZSSS_4n","ZSSS_10n","ZSSS_19n","ZSSS_26n","ZSSS_37n","ZSSS_6nRev",
                    "ZSSS_9nRev","ZSSS_14nRev", "ZSSS_18nRev", "ZSSS_22nRev")]

Analysis

Reliabity Scores

# SRP

cronbach.alpha(SRPTotA)

Cronbach's alpha for the 'SRPTotA' data-set

Items: 64
Sample units: 92
alpha: 0.884
cronbach.alpha(SRPIPMA)

Cronbach's alpha for the 'SRPIPMA' data-set

Items: 16
Sample units: 92
alpha: 0.797
cronbach.alpha(SRPICAA)

Cronbach's alpha for the 'SRPICAA' data-set

Items: 16
Sample units: 92
alpha: 0.752
cronbach.alpha(SRPELSA)

Cronbach's alpha for the 'SRPELSA' data-set

Items: 16
Sample units: 92
alpha: 0.788
cronbach.alpha(SRPASBA)

Cronbach's alpha for the 'SRPASBA' data-set

Items: 16
Sample units: 92
alpha: 0.713
# ICU 

cronbach.alpha(ICUTotA)

Cronbach's alpha for the 'ICUTotA' data-set

Items: 24
Sample units: 92
alpha: 0.802
cronbach.alpha(ICUCalA)

Cronbach's alpha for the 'ICUCalA' data-set

Items: 11
Sample units: 92
alpha: 0.395
cronbach.alpha(ICUUncareA)

Cronbach's alpha for the 'ICUUncareA' data-set

Items: 8
Sample units: 92
alpha: 0.778
cronbach.alpha(ICUUnemoA)

Cronbach's alpha for the 'ICUUnemoA' data-set

Items: 5
Sample units: 92
alpha: 0.888
# LSRP


cronbach.alpha(LevTotA)

Cronbach's alpha for the 'LevTotA' data-set

Items: 24
Sample units: 92
alpha: 0.827
cronbach.alpha(LevPrimA)

Cronbach's alpha for the 'LevPrimA' data-set

Items: 15
Sample units: 92
alpha: 0.804
cronbach.alpha(LevSecA)

Cronbach's alpha for the 'LevSecA' data-set

Items: 9
Sample units: 92
alpha: 0.664
# ZSSS


cronbach.alpha(SSSTotA)

Cronbach's alpha for the 'SSSTotA' data-set

Items: 40
Sample units: 92
alpha: 0.751
cronbach.alpha(SSSDISA)

Cronbach's alpha for the 'SSSDISA' data-set

Items: 10
Sample units: 92
alpha: 0.674
cronbach.alpha(SSSBorA)

Cronbach's alpha for the 'SSSBorA' data-set

Items: 10
Sample units: 92
alpha: 0.483
cronbach.alpha(SSSThrilA)

Cronbach's alpha for the 'SSSThrilA' data-set

Items: 10
Sample units: 92
alpha: 0.8
cronbach.alpha(SSSExpA)

Cronbach's alpha for the 'SSSExpA' data-set

Items: 10
Sample units: 92
alpha: 0.425

Table 1 (Descriptives)

Survey Means

# full 

FSDescriptives <- FSFSurveyT1 |> 
   summarise(
     across(
       .cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore, LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,SSSThrilTotal, SSSExpTotal
                 ),
       .fns = c( # this is used to describe the function within a list of the output (i.e., mean and sd)
         mean = \(x) mean(x, na.rm = T),
         sd = \(x) sd(x, na.rm =T)
       ),
       .names = '{.col} ---- {.fn}'
     )
   ) |> 
  pivot_longer(
    cols = everything()
  ) 
  

knitr::kable(FSDescriptives)
name value
SRPTotalScore —- mean 141.554348
SRPTotalScore —- sd 22.906098
SRPIPMTotal —- mean 37.684783
SRPIPMTotal —- sd 7.609897
SRPCATotal —- mean 36.945652
SRPCATotal —- sd 7.564178
SRPELSTotal —- mean 42.163044
SRPELSTotal —- sd 8.894099
SRPASBTotal —- mean 24.760870
SRPASBTotal —- sd 6.947795
ICUTotScore —- mean 42.717391
ICUTotScore —- sd 7.124214
ICUCalTotalScore —- mean 15.532609
ICUCalTotalScore —- sd 2.160846
ICUUncareTotalScore —- mean 14.315217
ICUUncareTotalScore —- sd 3.673275
ICUUnemoTotal —- mean 12.869565
ICUUnemoTotal —- sd 3.911711
LevTotalScore —- mean 46.532609
LevTotalScore —- sd 7.609206
LevPrimTotalScore —- mean 28.217391
LevPrimTotalScore —- sd 5.232666
LevSecTotalScore —- mean 18.315217
LevSecTotalScore —- sd 3.563951
SSSTotalScore —- mean 17.119565
SSSTotalScore —- sd 5.516892
SSSDISTotal —- mean 3.978261
SSSDISTotal —- sd 2.362613
SSSBorTotal —- mean 2.076087
SSSBorTotal —- sd 1.665643
SSSThrilTotal —- mean 6.086957
SSSThrilTotal —- sd 2.884792
SSSExpTotal —- mean 4.978261
SSSExpTotal —- sd 1.827616
# female 

 
FSDescriptivesFemale <- FSFSurveyT1 |> 
   filter(GenderNumb == "1") |> 
   summarise(
     across(
       .cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                 ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                 LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                 SSSThrilTotal, SSSExpTotal
       ),
       .fns = c( 
         mean = \(x) mean(x, na.rm = T),
         sd = \(x) sd(x, na.rm =T)
       ),
       .names = '{.col} ---- {.fn}'
     )
   ) |> 
   pivot_longer(
     cols = everything()
   )
 
knitr::kable(FSDescriptivesFemale)
name value
SRPTotalScore —- mean 138.985916
SRPTotalScore —- sd 23.912634
SRPIPMTotal —- mean 37.126761
SRPIPMTotal —- sd 8.095730
SRPCATotal —- mean 35.295775
SRPCATotal —- sd 6.776840
SRPELSTotal —- mean 41.915493
SRPELSTotal —- sd 9.401742
SRPASBTotal —- mean 24.647887
SRPASBTotal —- sd 6.911892
ICUTotScore —- mean 41.661972
ICUTotScore —- sd 7.197110
ICUCalTotalScore —- mean 15.211268
ICUCalTotalScore —- sd 1.948740
ICUUncareTotalScore —- mean 14.070422
ICUUncareTotalScore —- sd 3.896240
ICUUnemoTotal —- mean 12.380282
ICUUnemoTotal —- sd 3.822363
LevTotalScore —- mean 45.788732
LevTotalScore —- sd 7.882921
LevPrimTotalScore —- mean 27.591549
LevPrimTotalScore —- sd 5.172945
LevSecTotalScore —- mean 18.197183
LevSecTotalScore —- sd 3.804583
SSSTotalScore —- mean 16.774648
SSSTotalScore —- sd 5.695104
SSSDISTotal —- mean 4.000000
SSSDISTotal —- sd 2.420154
SSSBorTotal —- mean 1.971831
SSSBorTotal —- sd 1.698502
SSSThrilTotal —- mean 5.718310
SSSThrilTotal —- sd 2.889306
SSSExpTotal —- mean 5.084507
SSSExpTotal —- sd 1.688165
# male 
 
FSDescriptivesMale <- FSFSurveyT1 |> 
  filter(GenderNumb == "2") |> 
  summarise(
    across(
      .cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
                ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
                LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
                SSSThrilTotal, SSSExpTotal
      ),
      .fns = c( 
        mean = \(x) mean(x, na.rm = T),
        sd = \(x) sd(x, na.rm =T)
      ),
      .names = '{.col} ---- {.fn}'
    )
  ) |> 
  pivot_longer(
    cols = everything()
  )

knitr::kable(FSDescriptivesMale)
name value
SRPTotalScore —- mean 150.238095
SRPTotalScore —- sd 16.834205
SRPIPMTotal —- mean 39.571429
SRPIPMTotal —- sd 5.408987
SRPCATotal —- mean 42.523809
SRPCATotal —- sd 7.567160
SRPELSTotal —- mean 43.000000
SRPELSTotal —- sd 7.042727
SRPASBTotal —- mean 25.142857
SRPASBTotal —- sd 7.226934
ICUTotScore —- mean 46.285714
ICUTotScore —- sd 5.684566
ICUCalTotalScore —- mean 16.619048
ICUCalTotalScore —- sd 2.519448
ICUUncareTotalScore —- mean 15.142857
ICUUncareTotalScore —- sd 2.707133
ICUUnemoTotal —- mean 14.523810
ICUUnemoTotal —- sd 3.842122
LevTotalScore —- mean 49.047619
LevTotalScore —- sd 6.111270
LevPrimTotalScore —- mean 30.333333
LevPrimTotalScore —- sd 4.983306
LevSecTotalScore —- mean 18.714286
LevSecTotalScore —- sd 2.629503
SSSTotalScore —- mean 18.285714
SSSTotalScore —- sd 4.807732
SSSDISTotal —- mean 3.904762
SSSDISTotal —- sd 2.211442
SSSBorTotal —- mean 2.428571
SSSBorTotal —- sd 1.535299
SSSThrilTotal —- mean 7.333333
SSSThrilTotal —- sd 2.556039
SSSExpTotal —- mean 4.619048
SSSExpTotal —- sd 2.246691

ANS Means

As mentioned in the manuscript, some individuals SC that exceeded the maximum threshold of 9.99 of the NeuLog instrument. Therefore, there is sample number variation between HR, SC. Additionally, there are two tasks present which subdivided the sample further.

# Full 

## Baseline 

stat.desc(FSFHRT1$HRbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  92.0000000    0.0000000    0.0000000   44.3722222  117.0944444   72.7222222 
         sum       median         mean      SE.mean CI.mean.0.95          var 
6690.1944444   69.6916667   72.7195048    1.4816072    2.9430307  201.9547053 
     std.dev     coef.var 
  14.2110769    0.1954232 
stat.desc(FSFSCT1$SCbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  89.0000000    0.0000000    0.0000000    0.1524711    4.8621228    4.7096517 
         sum       median         mean      SE.mean CI.mean.0.95          var 
 145.8253472    1.3081056    1.6384870    0.1246785    0.2477724    1.3834813 
     std.dev     coef.var 
   1.1762148    0.7178664 
## Social Stressor 

stat.desc(SSFHRT1$SSHRCombAUCi)
      nbr.val      nbr.null        nbr.na           min           max 
 4.300000e+01  0.000000e+00  0.000000e+00 -2.201750e+04  8.673500e+03 
        range           sum        median          mean       SE.mean 
 3.069100e+04 -7.197100e+04 -1.750000e+02 -1.673744e+03  9.337483e+02 
 CI.mean.0.95           var       std.dev      coef.var 
 1.884380e+03  3.749109e+07  6.122997e+03 -3.658263e+00 
stat.desc(SSFSCT1$SSSCCombAUCi)
      nbr.val      nbr.null        nbr.na           min           max 
   41.0000000     0.0000000     0.0000000   -12.2550500  1136.6175000 
        range           sum        median          mean       SE.mean 
 1148.8725500 14644.2313000   337.5527000   357.1763732    41.9285410 
 CI.mean.0.95           var       std.dev      coef.var 
   84.7407423 72078.1044496   268.4736569     0.7516557 
## Countdown 

stat.desc(CDFHRT1$CDHRCombAuciSigaled)
      nbr.val      nbr.null        nbr.na           min           max 
    49.000000      0.000000      0.000000  -1785.500000   1071.000000 
        range           sum        median          mean       SE.mean 
  2856.500000 -14014.000000   -264.500000   -286.000000     72.785526 
 CI.mean.0.95           var       std.dev      coef.var 
   146.345108 259588.906250    509.498681     -1.781464 
stat.desc(CDFHRT1$CDHRCombAuciUnSigaled)
      nbr.val      nbr.null        nbr.na           min           max 
    49.000000      0.000000      0.000000  -1374.000000   1595.000000 
        range           sum        median          mean       SE.mean 
  2969.000000  -8893.500000   -136.000000   -181.500000     69.072386 
 CI.mean.0.95           var       std.dev      coef.var 
   138.879340 233778.729167    483.506700     -2.663949 
stat.desc(CDFSCT1$CDSCCombAuciSigaled)
     nbr.val     nbr.null       nbr.na          min          max        range 
   48.000000     0.000000     0.000000   -23.867700   255.289800   279.157500 
         sum       median         mean      SE.mean CI.mean.0.95          var 
 1118.414600     8.357850    23.300304     6.590098    13.257567  2084.610745 
     std.dev     coef.var 
   45.657538     1.959525 
stat.desc(CDFSCT1$CDSCCombAuciUnSigaled)
     nbr.val     nbr.null       nbr.na          min          max        range 
  48.0000000    0.0000000    0.0000000  -72.7636500  124.8127000  197.5763500 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  25.9418500   -0.5092500    0.5404552    3.9953236    8.0375544  766.2053218 
     std.dev     coef.var 
  27.6804140   51.2168513 
# Male 

## Baseline 

stat.desc(MHRbT1$HRbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  21.0000000    0.0000000    0.0000000   44.3722222   98.5500000   54.1777778 
         sum       median         mean      SE.mean CI.mean.0.95          var 
1399.8722222   66.8111111   66.6605820    2.8717408    5.9903464  173.1848022 
     std.dev     coef.var 
  13.1599697    0.1974176 
stat.desc(MSCbT1$SCbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  20.0000000    0.0000000    0.0000000    0.3249450    4.8621228    4.5371778 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  39.1885278    1.5854028    1.9594264    0.2768969    0.5795520    1.5334383 
     std.dev     coef.var 
   1.2383208    0.6319813 
## Social Stressor 

stat.desc(MSSHRT1$SSHRCombAUCi)
      nbr.val      nbr.null        nbr.na           min           max 
 1.000000e+01  0.000000e+00  0.000000e+00 -2.201750e+04  3.197000e+03 
        range           sum        median          mean       SE.mean 
 2.521450e+04 -3.635150e+04 -2.745000e+02 -3.635150e+03  2.788795e+03 
 CI.mean.0.95           var       std.dev      coef.var 
 6.308693e+03  7.777378e+07  8.818944e+03 -2.426019e+00 
stat.desc(MSSSCT1$SSSCCombAUCi)
      nbr.val      nbr.null        nbr.na           min           max 
 9.000000e+00  0.000000e+00  0.000000e+00 -1.225505e+01  1.136617e+03 
        range           sum        median          mean       SE.mean 
 1.148873e+03  3.096547e+03  3.375527e+02  3.440607e+02  1.105869e+02 
 CI.mean.0.95           var       std.dev      coef.var 
 2.550139e+02  1.100652e+05  3.317608e+02  9.642506e-01 
## Countdown 

stat.desc(MCDHRT1$CDHRCombAuciSigaled)
      nbr.val      nbr.null        nbr.na           min           max 
 1.100000e+01  0.000000e+00  0.000000e+00 -1.785500e+03  7.050000e+01 
        range           sum        median          mean       SE.mean 
 1.856000e+03 -5.791500e+03 -3.705000e+02 -5.265000e+02  1.524246e+02 
 CI.mean.0.95           var       std.dev      coef.var 
 3.396232e+02  2.555659e+05  5.055352e+02 -9.601808e-01 
stat.desc(MCDHRT1$CDHRCombAuciUnSigaled)
      nbr.val      nbr.null        nbr.na           min           max 
    11.000000      0.000000      0.000000   -702.000000    399.500000 
        range           sum        median          mean       SE.mean 
  1101.500000   -717.000000    -38.500000    -65.181818    111.047764 
 CI.mean.0.95           var       std.dev      coef.var 
   247.429836 135647.663636    368.303765     -5.650406 
stat.desc(MCDSCT1$CDSCCombAuciSigaled)
     nbr.val     nbr.null       nbr.na          min          max        range 
   11.000000     0.000000     0.000000   -23.867700    99.633050   123.500750 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  152.752550     7.089650    13.886595     9.735192    21.691358  1042.513495 
     std.dev     coef.var 
   32.287978     2.325118 
stat.desc(MCDSCT1$CDSCCombAuciUnSigaled)
     nbr.val     nbr.null       nbr.na          min          max        range 
   11.000000     0.000000     0.000000   -72.763650    29.732350   102.496000 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  -21.784750    -0.239500    -1.980432     8.857457    19.735643   862.999912 
     std.dev     coef.var 
   29.376860   -14.833563 
# Female 

## Baseline 

stat.desc(FemaleHRbaseT1$HRbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  71.0000000    0.0000000    0.0000000   48.9166667  117.0944444   68.1777778 
         sum       median         mean      SE.mean CI.mean.0.95          var 
5290.3222222   72.5888889   74.5115806    1.6732745    3.3372407  198.7891664 
     std.dev     coef.var 
  14.0992612    0.1892224 
stat.desc(FemaleSCbaseT1$SCbaseline)
     nbr.val     nbr.null       nbr.na          min          max        range 
  69.0000000    0.0000000    0.0000000    0.1524711    4.8173183    4.6648472 
         sum       median         mean      SE.mean CI.mean.0.95          var 
 106.6368194    1.1330900    1.5454612    0.1384621    0.2762968    1.3228510 
     std.dev     coef.var 
   1.1501526    0.7442132 
## Social Stressor 

stat.desc(FemaleSSHRT1$SSHRCombAUCi)
      nbr.val      nbr.null        nbr.na           min           max 
 3.300000e+01  0.000000e+00  0.000000e+00 -1.731050e+04  8.673500e+03 
        range           sum        median          mean       SE.mean 
 2.598400e+04 -3.561950e+04 -8.300000e+01 -1.079379e+03  8.836331e+02 
 CI.mean.0.95           var       std.dev      coef.var 
 1.799902e+03  2.576665e+07  5.076086e+03 -4.702785e+00 
stat.desc(FemaleSCSST1$SSSCCombAUCi)
      nbr.val      nbr.null        nbr.na           min           max 
   32.0000000     0.0000000     0.0000000    -0.9556500  1037.5249000 
        range           sum        median          mean       SE.mean 
 1038.4805500 11547.6845500   336.5123000   360.8651422    44.9082706 
 CI.mean.0.95           var       std.dev      coef.var 
   91.5910218 64536.0886273   254.0395415     0.7039736 
## Countdown 

stat.desc(FemaleHRCDT1$CDHRCombAuciSigaled)
      nbr.val      nbr.null        nbr.na           min           max 
    38.000000      0.000000      0.000000  -1345.000000   1071.000000 
        range           sum        median          mean       SE.mean 
  2416.000000  -8222.500000   -250.250000   -216.381579     80.380455 
 CI.mean.0.95           var       std.dev      coef.var 
   162.866273 245518.668030    495.498404     -2.289929 
stat.desc(FemaleHRCDT1$CDHRCombAuciUnSigaled)
      nbr.val      nbr.null        nbr.na           min           max 
    38.000000      0.000000      0.000000  -1374.000000   1595.000000 
        range           sum        median          mean       SE.mean 
  2969.000000  -8176.500000   -206.500000   -215.171053     82.944527 
 CI.mean.0.95           var       std.dev      coef.var 
   168.061575 261432.192923    511.304403     -2.376269 
stat.desc(FemaleSCCDT1$CDSCCombAuciSigaled)
     nbr.val     nbr.null       nbr.na          min          max        range 
   37.000000     0.000000     0.000000   -15.210900   255.289800   270.500700 
         sum       median         mean      SE.mean CI.mean.0.95          var 
  965.662050    12.799650    26.098974     8.048603    16.323323  2396.860237 
     std.dev     coef.var 
   48.957739     1.875849 
stat.desc(FemaleSCCDT1$CDSCCombAuciUnSigaled)
     nbr.val     nbr.null       nbr.na          min          max        range 
   37.000000     0.000000     0.000000   -39.534550   124.812700   164.347250 
         sum       median         mean      SE.mean CI.mean.0.95          var 
   47.726600    -0.691200     1.289908     4.526446     9.180058   758.082369 
     std.dev     coef.var 
   27.533296    21.345161 

t-tests

ANS

# baseline 

ind.t.test1<- t.test(HRbaseline ~ Gender, data = FSFHRT1)
ind.t.test1

    Welch Two Sample t-test

data:  HRbaseline by Gender
t = 2.3622, df = 34.741, p-value = 0.0239
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
  1.101805 14.600192
sample estimates:
mean in group Female mean in group Male   
            74.51158             66.66058 
ind.t.test1<- t.test(SCbaseline ~ Gender, data = FSFSCT1)
ind.t.test1

    Welch Two Sample t-test

data:  SCbaseline by Gender
t = -1.3372, df = 29.18, p-value = 0.1915
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -1.0469706  0.2190401
sample estimates:
mean in group Female mean in group Male   
            1.545461             1.959426 
#SS
ind.t.test1<- t.test(SSHRCombAUCi ~ Gender, data = SSFHRT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSHRCombAUCi by Gender
t = 0.87364, df = 10.867, p-value = 0.4012
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -3892.701  9004.243
sample estimates:
mean in group Female mean in group Male   
           -1079.379            -3635.150 
ind.t.test1<- t.test(SSSCCombAUCi ~ Gender, data = SSFSCT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSSCCombAUCi by Gender
t = 0.14079, df = 10.78, p-value = 0.8906
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -246.5540  280.1628
sample estimates:
mean in group Female mean in group Male   
            360.8651             344.0607 
# CD
ind.t.test1<- t.test(CDHRCombAuciSigaled ~ Gender, data = CDFHRT1)
ind.t.test1

    Welch Two Sample t-test

data:  CDHRCombAuciSigaled by Gender
t = 1.7997, df = 16.001, p-value = 0.0908
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -55.18278 675.41962
sample estimates:
mean in group Female mean in group Male   
           -216.3816            -526.5000 
ind.t.test1<- t.test(CDHRCombAuciUnSigaled ~ Gender, data = CDFHRT1)
ind.t.test1

    Welch Two Sample t-test

data:  CDHRCombAuciUnSigaled by Gender
t = -1.0821, df = 22.387, p-value = 0.2907
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -437.1508  137.1723
sample estimates:
mean in group Female mean in group Male   
          -215.17105            -65.18182 
ind.t.test1<- t.test(CDSCCombAuciSigaled ~ Gender, data = CDFSCT1)
ind.t.test1

    Welch Two Sample t-test

data:  CDSCCombAuciSigaled by Gender
t = 0.96682, df = 25.087, p-value = 0.3429
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -13.79805  38.22281
sample estimates:
mean in group Female mean in group Male   
            26.09897             13.88660 
ind.t.test1<- t.test(CDSCCombAuciUnSigaled ~ Gender, data = CDFSCT1)
ind.t.test1

    Welch Two Sample t-test

data:  CDSCCombAuciUnSigaled by Gender
t = 0.32878, df = 15.609, p-value = 0.7467
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -17.85937  24.40005
sample estimates:
mean in group Female mean in group Male   
            1.289908            -1.980432 

Survey

# SRP 


ind.t.test1<- t.test(SRPTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SRPTotalScore by Gender
t = -2.424, df = 46.285, p-value = 0.01932
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -20.594560  -1.909799
sample estimates:
mean in group Female mean in group Male   
            138.9859             150.2381 
ind.t.test1<- t.test(SRPIPMTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SRPIPMTotal by Gender
t = -1.6063, df = 49.122, p-value = 0.1146
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -5.5029328  0.6135968
sample estimates:
mean in group Female mean in group Male   
            37.12676             39.57143 
ind.t.test1<- t.test(SRPCATotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SRPCATotal by Gender
t = -3.9353, df = 30.13, p-value = 0.0004535
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -10.978471  -3.477599
sample estimates:
mean in group Female mean in group Male   
            35.29577             42.52381 
ind.t.test1<- t.test(SRPELSTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SRPELSTotal by Gender
t = -0.57104, df = 43.211, p-value = 0.5709
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -4.914022  2.745008
sample estimates:
mean in group Female mean in group Male   
            41.91549             43.00000 
ind.t.test1<- t.test(SRPASBTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SRPASBTotal by Gender
t = -0.27844, df = 31.625, p-value = 0.7825
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -4.117561  3.127621
sample estimates:
mean in group Female mean in group Male   
            24.64789             25.14286 
# ICU 


ind.t.test1<- t.test(ICUTotScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  ICUTotScore by Gender
t = -3.07, df = 40.837, p-value = 0.003796
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -7.665736 -1.581749
sample estimates:
mean in group Female mean in group Male   
            41.66197             46.28571 
ind.t.test1<- t.test(ICUCalTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  ICUCalTotalScore by Gender
t = -2.3603, df = 27.459, p-value = 0.02561
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -2.630642 -0.184918
sample estimates:
mean in group Female mean in group Male   
            15.21127             16.61905 
ind.t.test1<- t.test(ICUUncareTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  ICUUncareTotalScore by Gender
t = -1.4295, df = 46.976, p-value = 0.1595
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -2.5816516  0.4367824
sample estimates:
mean in group Female mean in group Male   
            14.07042             15.14286 
ind.t.test1<- t.test(ICUUnemoTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  ICUUnemoTotal by Gender
t = -2.2486, df = 32.625, p-value = 0.03142
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -4.0838237 -0.2032319
sample estimates:
mean in group Female mean in group Male   
            12.38028             14.52381 
# LSRP

ind.t.test1<- t.test(LevTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  LevTotalScore by Gender
t = -2.0005, df = 41.647, p-value = 0.05199
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -6.54718941  0.02941611
sample estimates:
mean in group Female mean in group Male   
            45.78873             49.04762 
ind.t.test1<- t.test(LevPrimTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  LevPrimTotalScore by Gender
t = -2.1956, df = 33.799, p-value = 0.03509
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -5.2801499 -0.2034181
sample estimates:
mean in group Female mean in group Male   
            27.59155             30.33333 
ind.t.test1<- t.test(LevSecTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  LevSecTotalScore by Gender
t = -0.70821, df = 47.259, p-value = 0.4823
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -1.9857677  0.9515625
sample estimates:
mean in group Female mean in group Male   
            18.19718             18.71429 
# SSS

ind.t.test1<- t.test(SSSTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSSTotalScore by Gender
t = -1.2108, df = 38.168, p-value = 0.2334
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -4.037142  1.015009
sample estimates:
mean in group Female mean in group Male   
            16.77465             18.28571 
ind.t.test1<- t.test(SSSDISTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSSDISTotal by Gender
t = 0.16959, df = 35.41, p-value = 0.8663
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -1.044363  1.234840
sample estimates:
mean in group Female mean in group Male   
            4.000000             3.904762 
ind.t.test1<- t.test(SSSBorTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSSBorTotal by Gender
t = -1.1681, df = 35.762, p-value = 0.2505
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -1.2498993  0.3364184
sample estimates:
mean in group Female mean in group Male   
            1.971831             2.428571 
ind.t.test1<- t.test(SSSThrilTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSSThrilTotal by Gender
t = -2.4666, df = 36.485, p-value = 0.01846
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -2.9422934 -0.2877535
sample estimates:
mean in group Female mean in group Male   
            5.718310             7.333333 
ind.t.test1<- t.test(SSSExpTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1

    Welch Two Sample t-test

data:  SSSExpTotal by Gender
t = 0.87885, df = 27.022, p-value = 0.3872
alternative hypothesis: true difference in means between group Female and group Male   is not equal to 0
95 percent confidence interval:
 -0.6211987  1.5521175
sample estimates:
mean in group Female mean in group Male   
            5.084507             4.619048 

Distributions of DVs

# Histogram function 

histo <- function(df, var, title = "Histogram", xlab = "DV", ylab = "Frequency", col = "honeydew", border = "black", bins = 5){
  df |> 
  ggplot(aes(x = {{var}})) +
    geom_histogram(binwidth = bins, fill = col, color = border) +
    labs(title = title, x = xlab, y = ylab)
}

SRP Full

Normal = SRPTot, SRPIPMTotal, SRPCATotal, SRPELSTotal
Non-Normal = SRPASBTotal

# SRPTot 

FSFSurveyT1 |> 
  histo(SRPTotalScore)

qqnorm(FSFSurveyT1$SRPTotalScore)
qqline(FSFSurveyT1$SRPTotalScore)

shapiro.test(FSFSurveyT1$SRPTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SRPTotalScore
W = 0.97854, p-value = 0.1335
# SRP IPM 

FSFSurveyT1 |> 
  histo(SRPIPMTotal)

qqnorm(FSFSurveyT1$SRPIPMTotal)
qqline(FSFSurveyT1$SRPIPMTotal)

shapiro.test(FSFSurveyT1$SRPIPMTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SRPIPMTotal
W = 0.99234, p-value = 0.8779
# SRPCATotal

FSFSurveyT1 |> 
  histo(SRPCATotal)

qqnorm(FSFSurveyT1$SRPCATotal)
qqline(FSFSurveyT1$SRPCATotal)

shapiro.test(FSFSurveyT1$SRPCATotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SRPCATotal
W = 0.98428, p-value = 0.3365
# SRPELSTotal

FSFSurveyT1 |> 
  histo(SRPELSTotal)

qqnorm(FSFSurveyT1$SRPELSTotal)
qqline(FSFSurveyT1$SRPELSTotal)

shapiro.test(FSFSurveyT1$SRPELSTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SRPELSTotal
W = 0.97861, p-value = 0.1352
# SRPASBTotal


FSFSurveyT1 |> 
  histo(SRPASBTotal)

qqnorm(FSFSurveyT1$SRPASBTotal)
qqline(FSFSurveyT1$SRPASBTotal)

shapiro.test(FSFSurveyT1$SRPASBTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SRPASBTotal
W = 0.93314, p-value = 0.0001476

ICU Full

Non-Normal = ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal

# ICUtotal 

FSFSurveyT1 |> 
  histo(ICUTotScore)

qqnorm(FSFSurveyT1$ICUTotScore)
qqline(FSFSurveyT1$ICUTotScore)

shapiro.test(FSFSurveyT1$ICUTotScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$ICUTotScore
W = 0.96482, p-value = 0.01386
# ICU cal 


FSFSurveyT1 |> 
  histo(ICUCalTotalScore)

qqnorm(FSFSurveyT1$ICUCalTotalScore)
qqline(FSFSurveyT1$ICUCalTotalScore)

shapiro.test(FSFSurveyT1$ICUCalTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$ICUCalTotalScore
W = 0.95915, p-value = 0.005679
# ICUUncare

FSFSurveyT1 |> 
  histo(ICUUncareTotalScore)

qqnorm(FSFSurveyT1$ICUUncareTotalScore)
qqline(FSFSurveyT1$ICUUncareTotalScore)

shapiro.test(FSFSurveyT1$ICUUncareTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$ICUUncareTotalScore
W = 0.96571, p-value = 0.01598
# Unemo 

FSFSurveyT1 |> 
  histo(ICUUnemoTotal)

qqnorm(FSFSurveyT1$ICUUnemoTotal)
qqline(FSFSurveyT1$ICUUnemoTotal)

shapiro.test(FSFSurveyT1$ICUUnemoTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$ICUUnemoTotal
W = 0.97151, p-value = 0.0413

Lev full

Normal = LevTotalScore, LevSecTotalScore
Non-Normal = LevPrimTotalScore

# Leve tot 


FSFSurveyT1 |> 
  histo(LevTotalScore)

qqnorm(FSFSurveyT1$LevTotalScore)
qqline(FSFSurveyT1$LevTotalScore)

shapiro.test(FSFSurveyT1$LevTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$LevTotalScore
W = 0.97816, p-value = 0.1254
# lev prim 


FSFSurveyT1 |> 
  histo(LevPrimTotalScore)

qqnorm(FSFSurveyT1$LevPrimTotalScore)
qqline(FSFSurveyT1$LevPrimTotalScore)

shapiro.test(FSFSurveyT1$LevPrimTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$LevPrimTotalScore
W = 0.96646, p-value = 0.01804
# lev sec

FSFSurveyT1 |> 
  histo(LevSecTotalScore)

qqnorm(FSFSurveyT1$LevSecTotalScore)
qqline(FSFSurveyT1$LevSecTotalScore)

shapiro.test(FSFSurveyT1$LevSecTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$LevSecTotalScore
W = 0.97963, p-value = 0.1598

SSS Full

Normal = SSSTotalScore
Non-Normal = SSSDISTotal, SSSBorTotal, SSSThrilTotal, SSSExpTotal

# SSS total 

FSFSurveyT1 |> 
  histo(SSSTotalScore)

qqnorm(FSFSurveyT1$SSSTotalScore)
qqline(FSFSurveyT1$SSSTotalScore)

shapiro.test(FSFSurveyT1$SSSTotalScore)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SSSTotalScore
W = 0.97996, p-value = 0.1689
# SSS dis 

FSFSurveyT1 |> 
  histo(SSSDISTotal)

qqnorm(FSFSurveyT1$SSSDISTotal)
qqline(FSFSurveyT1$SSSDISTotal)

shapiro.test(FSFSurveyT1$SSSDISTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SSSDISTotal
W = 0.95666, p-value = 0.003886
# SSSBorTotal

FSFSurveyT1 |> 
  histo(SSSBorTotal)

qqnorm(FSFSurveyT1$SSSBorTotal)
qqline(FSFSurveyT1$SSSBorTotal)

shapiro.test(FSFSurveyT1$SSSBorTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SSSBorTotal
W = 0.89337, p-value = 1.66e-06
# SSSThrilTotal
FSFSurveyT1 |> 
  histo(SSSThrilTotal)

qqnorm(FSFSurveyT1$SSSThrilTotal)
qqline(FSFSurveyT1$SSSThrilTotal)

shapiro.test(FSFSurveyT1$SSSThrilTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SSSThrilTotal
W = 0.92751, p-value = 7.295e-05
# SSS exp 

FSFSurveyT1 |> 
  histo(SSSExpTotal)

qqnorm(FSFSurveyT1$SSSExpTotal)
qqline(FSFSurveyT1$SSSExpTotal)

shapiro.test(FSFSurveyT1$SSSExpTotal)

    Shapiro-Wilk normality test

data:  FSFSurveyT1$SSSExpTotal
W = 0.96231, p-value = 0.009297

SRP Female

Normal = SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal
Non-Normal = SRPASBTotal

# SRPTot 

FemaleDisCheck |> 
  histo(SRPTotalScore)

qqnorm(FemaleDisCheck$SRPTotalScore)
qqline(FemaleDisCheck$SRPTotalScore)

shapiro.test(FemaleDisCheck$SRPTotalScore)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$SRPTotalScore
W = 0.98271, p-value = 0.4361
# SRP IPM 

FemaleDisCheck |> 
  histo(SRPIPMTotal)

qqnorm(FemaleDisCheck$SRPIPMTotal)
qqline(FemaleDisCheck$SRPIPMTotal)

shapiro.test(FemaleDisCheck$SRPIPMTotal)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$SRPIPMTotal
W = 0.99078, p-value = 0.8863
# SRPCATotal

FemaleDisCheck |> 
  histo(SRPCATotal)

qqnorm(FemaleDisCheck$SRPCATotal)
qqline(FemaleDisCheck$SRPCATotal)

shapiro.test(FemaleDisCheck$SRPCATotal)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$SRPCATotal
W = 0.98397, p-value = 0.5015
# SRPELSTotal

FemaleDisCheck |> 
  histo(SRPELSTotal)

qqnorm(FemaleDisCheck$SRPELSTotal)
qqline(FemaleDisCheck$SRPELSTotal)

shapiro.test(FemaleDisCheck$SRPELSTotal)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$SRPELSTotal
W = 0.97613, p-value = 0.1943
# SRPASBTotal


FemaleDisCheck |> 
  histo(SRPASBTotal)

qqnorm(FemaleDisCheck$SRPASBTotal)
qqline(FemaleDisCheck$SRPASBTotal)

shapiro.test(FemaleDisCheck$SRPASBTotal)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$SRPASBTotal
W = 0.93521, p-value = 0.0012

ICU Female

Normal = ICUUnemoTotal
Non-Normal = ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore,

# ICUtotal 

FemaleDisCheck |> 
  histo(ICUTotScore)

qqnorm(FemaleDisCheck$ICUTotScore)
qqline(FemaleDisCheck$ICUTotScore)

shapiro.test(FemaleDisCheck$ICUTotScore)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$ICUTotScore
W = 0.96252, p-value = 0.03254
# ICU cal 


FemaleDisCheck |> 
  histo(ICUCalTotalScore)

qqnorm(FemaleDisCheck$ICUCalTotalScore)
qqline(FemaleDisCheck$ICUCalTotalScore)

shapiro.test(FemaleDisCheck$ICUCalTotalScore)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$ICUCalTotalScore
W = 0.94579, p-value = 0.004055
# ICUUncare

FemaleDisCheck |> 
  histo(ICUUncareTotalScore)

qqnorm(FemaleDisCheck$ICUUncareTotalScore)
qqline(FemaleDisCheck$ICUUncareTotalScore)

shapiro.test(FemaleDisCheck$ICUUncareTotalScore)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$ICUUncareTotalScore
W = 0.94513, p-value = 0.003748
# Unemo 

FemaleDisCheck |> 
  histo(ICUUnemoTotal)

qqnorm(FemaleDisCheck$ICUUnemoTotal)
qqline(FemaleDisCheck$ICUUnemoTotal)

shapiro.test(FemaleDisCheck$ICUUnemoTotal)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$ICUUnemoTotal
W = 0.97343, p-value = 0.1366

Lev Female

Normal = LevTotalScore, LevSecTotalScore
Non-Normal = LevPrimTotalScore

# Leve tot 


FemaleDisCheck |> 
  histo(LevTotalScore)

qqnorm(FemaleDisCheck$LevTotalScore)
qqline(FemaleDisCheck$LevTotalScore)

shapiro.test(FemaleDisCheck$LevTotalScore)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$LevTotalScore
W = 0.97601, p-value = 0.1913
# lev prim 


FemaleDisCheck |> 
  histo(LevPrimTotalScore)

qqnorm(FemaleDisCheck$LevPrimTotalScore)
qqline(FemaleDisCheck$LevPrimTotalScore)

shapiro.test(FemaleDisCheck$LevPrimTotalScore)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$LevPrimTotalScore
W = 0.95639, p-value = 0.01485
# lev sec

FemaleDisCheck |> 
  histo(LevSecTotalScore)

qqnorm(FemaleDisCheck$LevSecTotalScore)
qqline(FemaleDisCheck$LevSecTotalScore)

shapiro.test(FemaleDisCheck$LevSecTotalScore)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$LevSecTotalScore
W = 0.97734, p-value = 0.227

SSS Female

Normal = SSSTotalScore
Non-Normal = SSSDISTotal, SSSBorTotal, SSSThrilTotal, SSSExpTotal

# SSS total 

FemaleDisCheck |> 
  histo(SSSTotalScore)

qqnorm(FemaleDisCheck$SSSTotalScore)
qqline(FemaleDisCheck$SSSTotalScore)

shapiro.test(FemaleDisCheck$SSSTotalScore)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$SSSTotalScore
W = 0.98541, p-value = 0.5828
# SSS dis 

FemaleDisCheck |> 
  histo(SSSDISTotal)

qqnorm(FemaleDisCheck$SSSDISTotal)
qqline(FemaleDisCheck$SSSDISTotal)

shapiro.test(FemaleDisCheck$SSSDISTotal)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$SSSDISTotal
W = 0.95206, p-value = 0.008654
# SSSBorTotal

FemaleDisCheck |> 
  histo(SSSBorTotal)

qqnorm(FemaleDisCheck$SSSBorTotal)
qqline(FemaleDisCheck$SSSBorTotal)

shapiro.test(FemaleDisCheck$SSSBorTotal)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$SSSBorTotal
W = 0.88064, p-value = 6.346e-06
# SSSThrilTotal
FemaleDisCheck |> 
  histo(SSSThrilTotal)

qqnorm(FemaleDisCheck$SSSThrilTotal)
qqline(FemaleDisCheck$SSSThrilTotal)

shapiro.test(FemaleDisCheck$SSSThrilTotal)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$SSSThrilTotal
W = 0.94616, p-value = 0.004233
# SSS exp 

FemaleDisCheck |> 
  histo(SSSExpTotal)

qqnorm(FemaleDisCheck$SSSExpTotal)
qqline(FemaleDisCheck$SSSExpTotal)

shapiro.test(FemaleDisCheck$SSSExpTotal)

    Shapiro-Wilk normality test

data:  FemaleDisCheck$SSSExpTotal
W = 0.96367, p-value = 0.03783

Table 2 (Partial Correlations)

Full

HR baseline

#SRP

pcor.test(FSFHRT1$SRPTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.02601355 0.8087933 -0.2427204 92  3 pearson
pcor.test(FSFHRT1$SRPIPMTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 0.004966072 0.9631606 0.04632101 92  3 pearson
pcor.test(FSFHRT1$SRPCATotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.06169229 0.5657486 0.5765256 92  3 pearson
pcor.test(FSFHRT1$SRPELSTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.06158436 0.5664299 -0.5755131 92  3 pearson
pcor.test(FSFHRT1$SRPASBTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.03117451 0.7718066 -0.2909178 92  3 spearman
# ICU

pcor.test(FSFHRT1$ICUTotScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate  p.value statistic  n gp   Method
1 0.08304044 0.439129 0.7772341 92  3 spearman
pcor.test(FSFHRT1$ICUCalTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.09684776 0.3665955 0.9076023 92  3 spearman
pcor.test(FSFHRT1$ICUUncareTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.05813552 0.5884013 0.5431707 92  3 spearman
pcor.test(FSFHRT1$ICUUnemoTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.04174293 0.6977168 0.3896918 92  3 spearman
# Lev

pcor.test(FSFHRT1$LevTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.03213252 0.7649938 0.2998671 92  3 pearson
pcor.test(FSFHRT1$LevPrimTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
     estimate   p.value   statistic  n gp   Method
1 -0.00209549 0.9844507 -0.01954547 92  3 spearman
pcor.test(FSFHRT1$LevSecTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.02490207 0.8168172 -0.2323431 92  3 pearson
# SSS 

pcor.test(FSFHRT1$SSSTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson")
     estimate p.value  statistic  n gp  Method
1 -0.08447431 0.43124 -0.7907503 92  3 pearson
pcor.test(FSFHRT1$SSSDISTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.05866318 0.5850148 0.5481177 92  3 spearman
pcor.test(FSFHRT1$SSSBorTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.07000974 0.5144435 -0.6546136 92  3 spearman
pcor.test(FSFHRT1$SSSThrilTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate p.value statistic  n gp   Method
1 -0.1334697 0.21242 -1.256162 92  3 spearman
pcor.test(FSFHRT1$SSSExpTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.06475256 0.5465938 0.6052419 92  3 spearman

SC baseline

#SRP

pcor.test(FSFSCT1$SRPTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age),method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1724679 0.1123041 -1.604741 89  3 pearson
pcor.test(FSFSCT1$SRPIPMTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 0.00512591 0.9626404 0.04698036 89  3 pearson
pcor.test(FSFSCT1$SRPCATotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
   estimate  p.value statistic  n gp  Method
1 -0.137153 0.207939  -1.26902 89  3 pearson
pcor.test(FSFSCT1$SRPELSTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
    estimate     p.value statistic  n gp  Method
1 -0.3137552 0.003263775 -3.028544 89  3 pearson
pcor.test(FSFSCT1$SRPASBTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
      estimate   p.value   statistic  n gp   Method
1 -0.002064273 0.9849503 -0.01891942 89  3 spearman
# ICU

pcor.test(FSFSCT1$ICUTotScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
      estimate   p.value   statistic  n gp   Method
1 -0.002713572 0.9802173 -0.02487039 89  3 spearman
pcor.test(FSFSCT1$ICUCalTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
    estimate    p.value statistic  n gp   Method
1 -0.2093414 0.05305946 -1.962121 89  3 spearman
pcor.test(FSFSCT1$ICUUncareTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
       estimate   p.value    statistic  n gp   Method
1 -0.0009128471 0.9933445 -0.008366385 89  3 spearman
pcor.test(FSFSCT1$ICUUnemoTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1374803 0.2068455  1.272107 89  3 spearman
# Lev

pcor.test(FSFSCT1$LevTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1052439 0.3348497 -0.9699629 89  3 pearson
pcor.test(FSFSCT1$LevPrimTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
     estimate   p.value statistic  n gp   Method
1 -0.08259219 0.4496411 -0.759565 89  3 spearman
pcor.test(FSFSCT1$LevSecTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1509196 0.1654266 -1.399228 89  3 pearson
# SSS 

pcor.test(FSFSCT1$SSSTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.2320159 0.03158946 -2.186116 89  3 pearson
pcor.test(FSFSCT1$SSSDISTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.09802261 0.3692456 -0.9027394 89  3 spearman
pcor.test(FSFSCT1$SSSBorTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1049607 0.3361592 -0.9673236 89  3 spearman
pcor.test(FSFSCT1$SSSThrilTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.06113907 0.5760202 -0.5613991 89  3 spearman
pcor.test(FSFSCT1$SSSExpTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.1450858 0.1825822 -1.343954 89  3 spearman

Social Stressor

## Heart Rate 
#SRP

pcor.test(SSFHRT1$SRPTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age),method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1706067 0.2925534 -1.067338 43  3 pearson
pcor.test(SSFHRT1$SRPIPMTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1318835 0.4172465 -0.8201484 43  3 pearson
pcor.test(SSFHRT1$SRPCATotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.2878343 0.07169856 -1.852737 43  3 pearson
pcor.test(SSFHRT1$SRPELSTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
    estimate  p.value statistic  n gp  Method
1 -0.1661689 0.305474 -1.038776 43  3 pearson
pcor.test(SSFHRT1$SRPASBTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1042375 0.5221086  0.646083 43  3 spearman
# ICU

pcor.test(SSFHRT1$ICUTotScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 0.01146363 0.9440301 0.07067122 43  3 spearman
pcor.test(SSFHRT1$ICUCalTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
    estimate  p.value statistic  n gp   Method
1 0.04159693 0.798839 0.2566428 43  3 spearman
pcor.test(SSFHRT1$ICUUncareTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
      estimate   p.value    statistic  n gp   Method
1 -0.001055788 0.9948412 -0.006508317 43  3 spearman
pcor.test(SSFHRT1$ICUUnemoTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.03018826 0.8532957 0.1861778 43  3 spearman
# Lev

pcor.test(SSFHRT1$LevTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
     estimate  p.value  statistic  n gp  Method
1 -0.02778026 0.864885 -0.1713151 43  3 pearson
pcor.test(SSFHRT1$LevPrimTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.01393588 0.9319852 0.0859149 43  3 spearman
pcor.test(SSFHRT1$LevSecTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.06299925 0.6993547 -0.3891264 43  3 pearson
# SSS 

pcor.test(SSFHRT1$SSSTotalScore, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1480328 0.3619847 -0.9227014 43  3 pearson
pcor.test(SSFHRT1$SSSDISTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
  estimate   p.value statistic  n gp   Method
1 0.060211 0.7120774 0.3718402 43  3 spearman
pcor.test(SSFHRT1$SSSBorTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.04264865 0.7938617 -0.2631434 43  3 spearman
pcor.test(SSFHRT1$SSSThrilTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
    estimate    p.value statistic  n gp   Method
1 -0.2786024 0.08171597 -1.788223 43  3 spearman
pcor.test(SSFHRT1$SSSExpTotal, SSFHRT1$SSHRCombAUCi, list(SSFHRT1$Female, SSFHRT1$White, SSFHRT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.2198053 0.1729382  1.388939 43  3 spearman
## Skin Conductance 

#SRP

pcor.test(SSFSCT1$SRPTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age),method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1218903 0.4659973 -0.7368359 41  3 pearson
pcor.test(SSFSCT1$SRPIPMTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.3811005 0.01824307 -2.473251 41  3 pearson
pcor.test(SSFSCT1$SRPCATotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2381381 0.1499379 -1.471152 41  3 pearson
pcor.test(SSFSCT1$SRPELSTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1329443 0.4262131 0.8048095 41  3 pearson
pcor.test(SSFSCT1$SRPASBTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1147594 0.4926722 0.6931358 41  3 spearman
# ICU

pcor.test(SSFSCT1$ICUTotScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 0.009246966 0.9560595 0.05548417 41  3 spearman
pcor.test(SSFSCT1$ICUCalTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
   estimate  p.value statistic  n gp   Method
1 0.0538958 0.747929 0.3238455 41  3 spearman
pcor.test(SSFSCT1$ICUUncareTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.0748645 0.6550864 -0.4504511 41  3 spearman
pcor.test(SSFSCT1$ICUUnemoTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
     estimate   p.value statistic  n gp   Method
1 -0.02436992 0.8845298 -0.146263 41  3 spearman
# Lev

pcor.test(SSFSCT1$LevTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.04409393 0.7926577 -0.2648211 41  3 pearson
pcor.test(SSFSCT1$LevPrimTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
     estimate   p.value statistic  n gp   Method
1 -0.06839993 0.6832452 -0.411363 41  3 spearman
pcor.test(SSFSCT1$LevSecTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.0341873 0.8385378 0.2052438 41  3 pearson
# SSS 

pcor.test(SSFSCT1$SSSTotalScore, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1502321 0.3679707 0.9117402 41  3 pearson
pcor.test(SSFSCT1$SSSDISTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.06763405 0.6866103 -0.4067357 41  3 spearman
pcor.test(SSFSCT1$SSSBorTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1554049 0.3515144 0.9438969 41  3 spearman
pcor.test(SSFSCT1$SSSThrilTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1104604 0.5091242  0.666843 41  3 spearman
pcor.test(SSFSCT1$SSSExpTotal, SSFSCT1$SSSCCombAUCi, list(SSFSCT1$Female, SSFSCT1$White, SSFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1574203 0.3452255 0.9564472 41  3 spearman

Countdown

## HR Signaled 

#SRP

pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson")
   estimate     p.value statistic  n gp  Method
1 -0.401147 0.005728543 -2.904878 49  3 pearson
pcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.3199755 0.03017364 -2.240257 49  3 pearson
pcor.test(CDFHRT1$SRPCATotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2087954 0.1637559 -1.416206 49  3 pearson
pcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.3476516 0.01791516 -2.459472 49  3 pearson
pcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate    p.value statistic  n gp   Method
1 -0.3333628 0.02357762 -2.345441 49  3 spearman
# ICU

pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1020763 0.4996575 -0.6806529 49  3 spearman
pcor.test(CDFHRT1$ICUCalTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.2154974 0.1503453 -1.463842 49  3 spearman
pcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 -0.098444 0.5151184 -0.656191 49  3 spearman
pcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.04755639 0.7536394 0.3158107 49  3 spearman
# Lev

pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1337553 0.3755109 -0.8952767 49  3 pearson
pcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.05625032 0.7104117 -0.3737141 49  3 spearman
pcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1708016 0.2564105 -1.149867 49  3 pearson
# SSS 

pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 -0.333356 0.0235806 -2.345387 49  3 pearson
pcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate    p.value statistic  n gp   Method
1 -0.3057151 0.03881397 -2.129856 49  3 spearman
pcor.test(CDFHRT1$SSSBorTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1232979 0.4142971 -0.8241545 49  3 spearman
pcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate  p.value  statistic  n gp   Method
1 -0.1461885 0.332329 -0.9802358 49  3 spearman
pcor.test(CDFHRT1$SSSExpTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.2081389 0.1651149 -1.411551 49  3 spearman
## HR Unsignaled 

#SRP

pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1740436 0.2473582  1.172367 49  3 pearson
pcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate  p.value statistic  n gp  Method
1 0.07598876 0.615721  0.505514 49  3 pearson
pcor.test(CDFHRT1$SRPCATotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
  estimate   p.value statistic  n gp  Method
1 0.173424 0.2490713  1.168064 49  3 pearson
pcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1393526 0.3556727 0.9334684 49  3 pearson
pcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.07038923 0.6420447 0.4680703 49  3 spearman
# ICU

pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.02240489 0.8825055 0.1486545 49  3 spearman
pcor.test(CDFHRT1$ICUCalTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1252307 0.4069614 0.8372775 49  3 spearman
pcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 0.002311066 0.9878383 0.01532992 49  3 spearman
pcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.04180003 0.7826863 -0.2775126 49  3 spearman
# Lev

pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.02899293 0.8483155 0.1923982 49  3 pearson
pcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1406559 0.3511469  0.942374 49  3 spearman
pcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.03643697 0.8100153 0.2418561 49  3 pearson
# SSS 

pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1722155 0.2524357  1.159675 49  3 pearson
pcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1685004 0.2629693  1.133918 49  3 spearman
pcor.test(CDFHRT1$SSSBorTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
   estimate    p.value statistic  n gp   Method
1 0.2702601 0.06929107  1.861993 49  3 spearman
pcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1351967 0.3703401 0.9051036 49  3 spearman
pcor.test(CDFHRT1$SSSExpTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1075471 0.4768282 -0.7175485 49  3 spearman
## SC signaled 



#SRP

pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.09964416 0.5148868 -0.6566786 48  3 pearson
pcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate  p.value  statistic  n gp  Method
1 -0.0962247 0.529485 -0.6339292 48  3 pearson
pcor.test(CDFSCT1$SRPCATotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.01947212 0.8989727 -0.1277114 48  3 pearson
pcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.0814333 0.5948759 -0.5357733 48  3 pearson
pcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.08153656 0.5944073 0.5364572 48  3 spearman
# ICU

pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.06143973 0.6884706 -0.4036498 48  3 spearman
pcor.test(CDFSCT1$ICUCalTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1007967 0.5100122  0.664352 48  3 spearman
pcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.01927312 0.8999999 0.1264058 48  3 spearman
pcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1495447 0.3268519 -0.9917828 48  3 spearman
# Lev

pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate  p.value  statistic  n gp  Method
1 -0.1017953 0.505808 -0.6710018 48  3 pearson
pcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1340788 0.3798937 0.8872245 48  3 spearman
pcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1855536 0.2223378 -1.238259 48  3 pearson
# SSS 

pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.04003091 0.7940281 0.2627108 48  3 pearson
pcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
  estimate   p.value statistic  n gp   Method
1 0.164949 0.2788917  1.096665 48  3 spearman
pcor.test(CDFSCT1$SSSBorTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate    p.value statistic  n gp   Method
1 0.3265378 0.02857926  2.265433 48  3 spearman
pcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 0.01341004 0.9303299 0.08794342 48  3 spearman
pcor.test(CDFSCT1$SSSExpTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1778328 0.2425193  1.185016 48  3 spearman
## SC Unsignaled 



#SRP

pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.02509109 0.8700418 0.1645851 48  3 pearson
pcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1762964 0.2466788  1.174448 48  3 pearson
pcor.test(CDFSCT1$SRPCATotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.03363885 0.8263632 0.2207096 48  3 pearson
pcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.02189283 0.8864909 -0.1435953 48  3 pearson
pcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.1704498 0.2629472 -1.134313 48  3 spearman
# ICU

pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1248648 0.4137834 -0.8252522 48  3 spearman
pcor.test(CDFSCT1$ICUCalTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.09370631 0.5403646 -0.6171891 48  3 spearman
pcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.0613446 0.6889286 -0.4030225 48  3 spearman
pcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
    estimate  p.value  statistic  n gp   Method
1 -0.0443013 0.772611 -0.2907886 48  3 spearman
# Lev

pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.08157799 0.5942194 0.5367316 48  3 pearson
pcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.02536905 0.8686147 -0.1664095 48  3 spearman
pcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.02855391 0.8522933 0.1873169 48  3 pearson
# SSS 

pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson")
  estimate   p.value statistic  n gp  Method
1 0.148852 0.3291231 0.9870845 48  3 pearson
pcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.2203832 0.1457446  1.481576 48  3 spearman
pcor.test(CDFSCT1$SSSBorTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate    p.value statistic  n gp   Method
1 0.3146555 0.03527598  2.173748 48  3 spearman
pcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate  p.value statistic  n gp   Method
1 0.1986524 0.190811  1.329141 48  3 spearman
pcor.test(CDFSCT1$SSSExpTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1119332 0.4641402 0.7386371 48  3 spearman

Female Only

HR baseline

# SRP

pcor.test(FemaleHRbaseT1$SRPTot, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 0.007961143 0.9482352 0.06516683 71  2 pearson
pcor.test(FemaleHRbaseT1$SRPIPM, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.02836029 0.8170654 0.2322324 71  2 pearson
pcor.test(FemaleHRbaseT1$SRPCA, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1124774 0.3574877 0.9265464 71  2 pearson
pcor.test(FemaleHRbaseT1$SRPELS, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.06303849 0.6068444 -0.5170206 71  2 pearson
pcor.test(FemaleHRbaseT1$SRPASB, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.04716191 0.7003756 0.3864669 71  2 spearman
# ICU

pcor.test(FemaleHRbaseT1$ICUTot, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1767543 0.1462585   1.46994 71  2 spearman
pcor.test(FemaleHRbaseT1$ICUCal, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1486528 0.2228325  1.230447 71  2 spearman
pcor.test(FemaleHRbaseT1$ICUUncare, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1552206 0.2028269  1.286123 71  2 spearman
pcor.test(FemaleHRbaseT1$ICUUnemo, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1406379 0.2490649  1.162727 71  2 pearson
# Lev

pcor.test(FemaleHRbaseT1$LevTot, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.03947964 0.7473944 0.3234069 71  2 pearson
pcor.test(FemaleHRbaseT1$LevPrim, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 0.005765758 0.9624978 0.04719555 71  2 spearman
pcor.test(FemaleHRbaseT1$LevSec, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson")
      estimate   p.value  statistic  n gp  Method
1 -0.005179169 0.9663108 -0.0423939 71  2 pearson
# SSS 

pcor.test(FemaleHRbaseT1$SSSTot, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.06253916 0.6097019 -0.5129091 71  2 pearson
pcor.test(FemaleHRbaseT1$SSSDIS, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.0476913 0.6971741 0.3908148 71  2 spearman
pcor.test(FemaleHRbaseT1$SSSBor, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.05905643 0.6297927 -0.4842429 71  2 spearman
pcor.test(FemaleHRbaseT1$SSSThril, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.06631459 0.5882433 -0.5440058 71  2 spearman
pcor.test(FemaleHRbaseT1$SSSExp, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.0529366 0.6657456 0.4339132 71  2 spearman

SC baseline

#SRP

pcor.test(FemaleSCbaseT1$SRPTot, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1870743 0.1295523 -1.535347 69  2 pearson
pcor.test(FemaleSCbaseT1$SRPIPM, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson")
      estimate   p.value   statistic  n gp  Method
1 -0.008563325 0.9451679 -0.06904227 69  2 pearson
pcor.test(FemaleSCbaseT1$SRPCA, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1230658 0.3211205 -0.9997882 69  2 pearson
pcor.test(FemaleSCbaseT1$SRPELS, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson")
    estimate     p.value statistic  n gp  Method
1 -0.3316084 0.006120008  -2.83386 69  2 pearson
pcor.test(FemaleSCbaseT1$SRPASB, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 0.007109186 0.9544681 0.05731754 69  2 spearman
# ICU

pcor.test(FemaleSCbaseT1$ICUTot, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 0.006168834 0.9604855 0.04973568 69  2 spearman
pcor.test(FemaleSCbaseT1$ICUCal, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.1530214 0.2163625   -1.2484 69  2 spearman
pcor.test(FemaleSCbaseT1$ICUUncare, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.08918342 0.4729473 -0.7218963 69  2 spearman
pcor.test(FemaleSCbaseT1$ICUUnemo, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1927545 0.1181076  1.583736 69  2 pearson
# Lev

pcor.test(FemaleSCbaseT1$LevTot, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1800692 0.1448018 -1.475889 69  2 pearson
pcor.test(FemaleSCbaseT1$LevPrim, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.1599716 0.1959669 -1.306558 69  2 spearman
pcor.test(FemaleSCbaseT1$LevSec, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.2070063 0.09280329 -1.705889 69  2 pearson
# SSS 

pcor.test(FemaleSCbaseT1$SSSTot, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson")
    estimate     p.value statistic  n gp  Method
1 -0.3143761 0.009572456 -2.669952 69  2 pearson
pcor.test(FemaleSCbaseT1$SSSDIS, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman")
    estimate    p.value statistic  n gp   Method
1 -0.2475152 0.04344437 -2.059618 69  2 spearman
pcor.test(FemaleSCbaseT1$SSSBor, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.05325435 0.6686466 -0.4299604 69  2 spearman
pcor.test(FemaleSCbaseT1$SSSThril, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1143652 0.3567731 -0.9281316 69  2 spearman
pcor.test(FemaleSCbaseT1$SSSExp, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.1717418 0.1646317  -1.40551 69  2 spearman

Social Stressor

## Heart Rate 
#SRP
pcor.test(FemaleSSHRT1$SRPTot, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1152179 0.5370995 -0.6246274 33  2 pearson
pcor.test(FemaleSSHRT1$SRPIPM, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.09898992 0.5962453 -0.5357082 33  2 pearson
pcor.test(FemaleSSHRT1$SRPCA, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1705305 0.3590384 -0.9319861 33  2 pearson
pcor.test(FemaleSSHRT1$SRPELS, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2087584 0.2597315 -1.149526 33  2 pearson
pcor.test(FemaleSSHRT1$SRPASB, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1769388 0.3409897   0.96812 33  2 spearman
# ICU

pcor.test(FemaleSSHRT1$ICUTot, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "spearman")
     estimate   p.value   statistic  n gp   Method
1 -0.01699671 0.9276903 -0.09154333 33  2 spearman
pcor.test(FemaleSSHRT1$ICUCal, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.04091691 0.8270044  0.220529 33  2 spearman
pcor.test(FemaleSSHRT1$ICUUncare, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 0.009929855 0.9577189 0.05347654 33  2 spearman
pcor.test(FemaleSSHRT1$ICUUnemo, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1103085 0.5546944 -0.5976769 33  2 pearson
# Lev

pcor.test(FemaleSSHRT1$LevTot, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.03610499 0.8470954 -0.1945582 33  2 pearson
pcor.test(FemaleSSHRT1$LevPrim, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "spearman")
   estimate  p.value statistic  n gp   Method
1 0.1637044 0.378876 0.8936307 33  2 spearman
pcor.test(FemaleSSHRT1$LevSec, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 -0.204287 0.2703068 -1.123819 33  2 pearson
# SSS 

pcor.test(FemaleSSHRT1$SSSTot, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1393905 0.4545427 -0.7580413 33  2 pearson
pcor.test(FemaleSSHRT1$SSSDIS, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.02421787 0.8971068 -0.1304555 33  2 spearman
pcor.test(FemaleSSHRT1$SSSBor, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.03869794 0.8362567  0.208551 33  2 spearman
pcor.test(FemaleSSHRT1$SSSThril, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1601264 0.3895242 -0.8735793 33  2 spearman
pcor.test(FemaleSSHRT1$SSSExp, FemaleSSHRT1$SSHRCombAUCi, list(FemaleSSHRT1$White, FemaleSSHRT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.06056932 0.7461844 0.3267757 33  2 spearman
## Skin Conductance 

#SRP

pcor.test(FemaleSCSST1$SRPTot, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1516193 0.4238232 -0.811678 32  2 pearson
pcor.test(FemaleSCSST1$SRPIPM, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.3810148 0.03777205 -2.180628 32  2 pearson
pcor.test(FemaleSCSST1$SRPCA, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2252578 0.2313772 -1.223394 32  2 pearson
pcor.test(FemaleSCSST1$SRPELS, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1223499 0.5195174 0.6523154 32  2 pearson
pcor.test(FemaleSCSST1$SRPASB, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.03522022 0.8534101 0.1864836 32  2 spearman
# ICU

pcor.test(FemaleSCSST1$ICUTot, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1316993 0.4878538 -0.7030104 32  2 spearman
pcor.test(FemaleSCSST1$ICUCal, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.07695838 0.6860599 -0.4084368 32  2 spearman
pcor.test(FemaleSCSST1$ICUUncare, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "spearman")
     estimate   p.value statistic  n gp   Method
1 -0.07571866 0.6908679 -0.401819 32  2 spearman
pcor.test(FemaleSCSST1$ICUUnemo, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1458343 0.4419222 -0.7800219 32  2 pearson
# Lev

pcor.test(FemaleSCSST1$LevTot, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1326187 0.4847939 -0.7080062 32  2 pearson
pcor.test(FemaleSCSST1$LevPrim, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1511654 0.4252285 -0.8091907 32  2 spearman
pcor.test(FemaleSCSST1$LevSec, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.08565238 0.6526876 -0.4549015 32  2 pearson
# SSS 

pcor.test(FemaleSCSST1$SSSTot, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.2922458 0.1170878  1.617013 32  2 pearson
pcor.test(FemaleSCSST1$SSSDIS, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.08875268 0.6409401 -0.4714957 32  2 spearman
pcor.test(FemaleSCSST1$SSSBor, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.2817043 0.1315217  1.553556 32  2 spearman
pcor.test(FemaleSCSST1$SSSThril, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.2250753 0.2317654   1.22235 32  2 spearman
pcor.test(FemaleSCSST1$SSSExp, FemaleSCSST1$SSSCCombAUCi, list(FemaleSCSST1$White, FemaleSCSST1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1324802 0.4852545  0.707253 32  2 spearman

Countdown

## HR Signaled 

#SRP
pcor.test(FemaleHRCDT1$SRPTot, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.3687648 0.02688685 -2.313283 38  2 pearson
pcor.test(FemaleHRCDT1$SRPIPM, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.3019329 0.07350318 -1.846745 38  2 pearson
pcor.test(FemaleHRCDT1$SRPCA, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2346641 0.1683209 -1.407621 38  2 pearson
pcor.test(FemaleHRCDT1$SRPELS, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.3266299 0.05185733 -2.015086 38  2 pearson
pcor.test(FemaleHRCDT1$SRPASB, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
    estimate    p.value statistic  n gp   Method
1 -0.3159753 0.06046844 -1.941927 38  2 spearman
# ICU

pcor.test(FemaleHRCDT1$ICUTot, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.05017003 0.7713728 -0.2929079 38  2 spearman
pcor.test(FemaleHRCDT1$ICUCal, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
    estimate  p.value statistic  n gp   Method
1 -0.2124339 0.213545 -1.267625 38  2 spearman
pcor.test(FemaleHRCDT1$ICUUncare, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.08160535 0.6361114 -0.4774292 38  2 spearman
pcor.test(FemaleHRCDT1$ICUUnemo, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
    estimate  p.value statistic  n gp  Method
1 0.05913598 0.731904 0.3454235 38  2 pearson
# Lev

pcor.test(FemaleHRCDT1$LevTot, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.06647224 0.7001008 -0.3884556 38  2 pearson
pcor.test(FemaleHRCDT1$LevPrim, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.02536724 0.8832462 -0.1479628 38  2 spearman
pcor.test(FemaleHRCDT1$LevSec, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.06850159 0.6913887 -0.4003699 38  2 pearson
# SSS 

pcor.test(FemaleHRCDT1$SSSTot, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
    estimate    p.value statistic  n gp  Method
1 -0.3075942 0.06800677 -1.884954 38  2 pearson
pcor.test(FemaleHRCDT1$SSSDIS, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 -0.3283233 0.0505838 -2.026792 38  2 spearman
pcor.test(FemaleHRCDT1$SSSBor, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1647106 0.3370734 -0.9737185 38  2 spearman
pcor.test(FemaleHRCDT1$SSSThril, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1405197 0.4136825 -0.8275747 38  2 spearman
pcor.test(FemaleHRCDT1$SSSExp, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1657183 0.3340843 -0.9798439 38  2 spearman
## HR Unsignaled 

#SRP

pcor.test(FemaleHRCDT1$SRPTot, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1105462 0.5209766 0.6485646 38  2 pearson
pcor.test(FemaleHRCDT1$SRPIPM, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 0.006528615 0.9698554 0.03806885 38  2 pearson
pcor.test(FemaleHRCDT1$SRPCA, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.0503173 0.7707197 0.2937699 38  2 pearson
pcor.test(FemaleHRCDT1$SRPELS, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.1399424 0.4156228 0.8241069 38  2 pearson
pcor.test(FemaleHRCDT1$SRPASB, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.02521714 0.8839323 0.1470867 38  2 spearman
# ICU

pcor.test(FemaleHRCDT1$ICUTot, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1085369 0.5286261 -0.6366347 38  2 spearman
pcor.test(FemaleHRCDT1$ICUCal, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.0254988 0.8826448 -0.1487306 38  2 spearman
pcor.test(FemaleHRCDT1$ICUUncare, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
     estimate  p.value  statistic  n gp   Method
1 -0.09435372 0.584127 -0.5526375 38  2 spearman
pcor.test(FemaleHRCDT1$ICUUnemo, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.2045449 0.2314394 -1.218453 38  2 pearson
# Lev

pcor.test(FemaleHRCDT1$LevTot, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.0300487 0.8618902 -0.1752917 38  2 pearson
pcor.test(FemaleHRCDT1$LevPrim, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.0305373 0.8596666 0.1781446 38  2 spearman
pcor.test(FemaleHRCDT1$LevSec, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
     estimate  p.value   statistic  n gp  Method
1 -0.00270151 0.987524 -0.01575243 38  2 pearson
# SSS 

pcor.test(FemaleHRCDT1$SSSTot, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson")
  estimate   p.value statistic  n gp  Method
1 0.152125 0.3757747 0.8974792 38  2 pearson
pcor.test(FemaleHRCDT1$SSSDIS, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1599198 0.3515067  0.944642 38  2 spearman
pcor.test(FemaleHRCDT1$SSSBor, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
  estimate   p.value statistic  n gp   Method
1 0.244036 0.1514779  1.467325 38  2 spearman
pcor.test(FemaleHRCDT1$SSSThril, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1389461 0.4189835 0.8181238 38  2 spearman
pcor.test(FemaleHRCDT1$SSSExp, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.0635337 0.7127829 -0.3712119 38  2 spearman
## SC signaled 



#SRP


pcor.test(FemaleSCCDT1$SRPTot, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1163561 0.5056419 -0.6729863 37  2 pearson
pcor.test(FemaleSCCDT1$SRPIPM, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1283683 0.4623971 -0.7435718 37  2 pearson
pcor.test(FemaleSCCDT1$SRPCA, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
   estimate   p.value  statistic  n gp  Method
1 -0.132148 0.4492095 -0.7658492 37  2 pearson
pcor.test(FemaleSCCDT1$SRPELS, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
     estimate  p.value  statistic  n gp  Method
1 -0.05289124 0.762838 -0.3042629 37  2 pearson
pcor.test(FemaleSCCDT1$SRPASB, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.04988003 0.7759856 0.2868961 37  2 spearman
# ICU

pcor.test(FemaleSCCDT1$ICUTot, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1204197 0.4907896 -0.6968292 37  2 spearman
pcor.test(FemaleSCCDT1$ICUCal, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1768983 0.3093521  1.032487 37  2 spearman
pcor.test(FemaleSCCDT1$ICUUncare, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 -0.05323801 0.7613281 -0.3062634 37  2 spearman
pcor.test(FemaleSCCDT1$ICUUnemo, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 -0.1785845 0.3046884 -1.042651 37  2 pearson
# Lev

pcor.test(FemaleSCCDT1$LevTot, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.06665225 0.7036315 -0.3837414 37  2 pearson
pcor.test(FemaleSCCDT1$LevPrim, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1946556 0.2624859  1.140018 37  2 spearman
pcor.test(FemaleSCCDT1$LevSec, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1636033 0.3476859 -0.9526654 37  2 pearson
# SSS 

pcor.test(FemaleSCCDT1$SSSTot, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.0402785 0.8183017 0.2315703 37  2 pearson
pcor.test(FemaleSCCDT1$SSSDIS, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.2368987 0.1706217  1.400753 37  2 spearman
pcor.test(FemaleSCCDT1$SSSBor, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
   estimate    p.value statistic  n gp   Method
1 0.3218425 0.05937472  1.952744 37  2 spearman
pcor.test(FemaleSCCDT1$SSSThril, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
     estimate   p.value   statistic  n gp   Method
1 -0.01009044 0.9541236 -0.05796811 37  2 spearman
pcor.test(FemaleSCCDT1$SSSExp, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1480837 0.3959072 0.8601597 37  2 spearman
## SC Unsignaled 



#SRP

pcor.test(FemaleSCCDT1$SRPTot, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
     estimate   p.value   statistic  n gp  Method
1 -0.01052575 0.9521467 -0.06046918 37  2 pearson
pcor.test(FemaleSCCDT1$SRPIPM, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.2059393 0.2352748  1.208945 37  2 pearson
pcor.test(FemaleSCCDT1$SRPCA, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.04704468 0.7884216 0.2705507 37  2 pearson
pcor.test(FemaleSCCDT1$SRPELS, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
    estimate   p.value  statistic  n gp  Method
1 -0.1000495 0.5674288 -0.5776387 37  2 pearson
pcor.test(FemaleSCCDT1$SRPASB, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
  estimate    p.value statistic  n gp   Method
1 -0.28829 0.09305933  -1.72953 37  2 spearman
# ICU

pcor.test(FemaleSCCDT1$ICUTot, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1571517 0.3672817 -0.9141261 37  2 spearman
pcor.test(FemaleSCCDT1$ICUCal, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
     estimate   p.value   statistic  n gp   Method
1 -0.01075126 0.9511227 -0.06176483 37  2 spearman
pcor.test(FemaleSCCDT1$ICUUncare, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
    estimate   p.value  statistic  n gp   Method
1 -0.1084658 0.5351116 -0.6267866 37  2 spearman
pcor.test(FemaleSCCDT1$ICUUnemo, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
    estimate   p.value statistic  n gp  Method
1 0.01871194 0.9150348 0.1075108 37  2 pearson
# Lev

pcor.test(FemaleSCCDT1$LevTot, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.0705266 0.6872499 0.4061558 37  2 pearson
pcor.test(FemaleSCCDT1$LevPrim, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
     estimate   p.value  statistic  n gp   Method
1 0.006708969 0.9694886 0.03854096 37  2 spearman
pcor.test(FemaleSCCDT1$LevSec, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
     estimate   p.value  statistic  n gp  Method
1 -0.06266915 0.7206107 -0.3607159 37  2 pearson
# SSS 

pcor.test(FemaleSCCDT1$SSSTot, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson")
   estimate   p.value statistic  n gp  Method
1 0.0874113 0.6175618 0.5040691 37  2 pearson
pcor.test(FemaleSCCDT1$SSSDIS, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.2719087 0.1140728  1.623152 37  2 spearman
pcor.test(FemaleSCCDT1$SSSBor, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
   estimate    p.value statistic  n gp   Method
1 0.3551021 0.03632389   2.18212 37  2 spearman
pcor.test(FemaleSCCDT1$SSSThril, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
   estimate   p.value statistic  n gp   Method
1 0.1926206 0.2676052  1.127638 37  2 spearman
pcor.test(FemaleSCCDT1$SSSExp, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman")
    estimate   p.value statistic  n gp   Method
1 0.03493665 0.8420732 0.2008184 37  2 spearman